Run and Install OmniCoder‑9B Locally: Complete 2026 Guide

OmniCoder-9B is a 9‑billion‑parameter open‑weight coding agent built on Alibaba’s Qwen3.5‑9B architecture and fine‑tuned on more than 425,000 end‑to‑end “agentic” coding trajectories from models like Claude Opus 4.6, GPT‑5.4, GPT‑5.3‑Codex, and Gemini 3.1 Pro.

Despite its relatively small size, OmniCoder‑9B reaches 83.8 percent pass@1 on GPQA Diamond and 90 percent pass@5 on AIME 2025, matching or beating much larger long‑context and reasoning models on several benchmarks while remaining practical to run locally on consumer‑grade GPUs or mid‑range cloud GPUs.

The model exposes standard Hugging Face, vLLM, llama.cpp (GGUF), and Ollama entry points, and ships with recommended hyperparameters and quantized variants around 5.7–9.5 GB that work well on 8–16 GB VRAM cards.​

Quick Comparison Table

Example Benchmark

• GPQA Diamond pass@1: 83.8% (vs 81.7% for Qwen3.5‑9B base).
• AIME 2025 pass@5: 90%, close to much larger reasoning models.
• Terminal‑Bench 2.0: 23.6%, ~61% higher than Qwen3.5‑9B base (14.6%).

1. What OmniCoder‑9B is and why it matters

1.1 Model overview

OmniCoder‑9B is a dense 9B‑parameter language model derived from Qwen3.5‑9B, which itself is a hybrid architecture that interleaves Gated Delta Networks (a linear‑attention variant) with standard attention blocks to support efficient long‑context reasoning.

Instead of training on general web text, OmniCoder‑9B is fine‑tuned on more than 425,000 “agentic trajectories” collected from production coding agents such as Claude Code, OpenCode, Codex, and Droid, where each trajectory includes prompts, tool calls, file reads, edits, compiler errors, and corrections across an entire coding task.

These trajectories were generated and filtered from high‑end models including Claude Opus 4.6, GPT‑5.4, GPT‑5.3‑Codex, and Gemini 3.1 Pro, effectively distilling their coding behaviors into a smaller open model.

Key architectural and training facts:

• Base: Qwen3.5‑9B dense model with 9B active parameters.
• Context: 262K native tokens with extension options beyond 1M via RoPE scaling in supported runtimes.
• Training method: LoRA supervised fine‑tuning (r=64, alpha=32) on the curated trajectory dataset using Axolotl on 4× NVIDIA H200 GPUs in bf16 precision.​
• License: Apache 2.0, meaning the weights can be used commercially with minimal restrictions.​

1.2 Behavioral focus: agentic coding, not just code completion

The OmniCoder‑9B authors emphasize that the model was trained on what frontier agents do when editing real codebases, not on generic code samples. Several behaviors repeatedly highlighted in the model card, Ollama page, and community tests include:

• Read‑before‑write: The model tends to open and inspect existing files and function definitions before proposing changes, which reduces the risk of clobbering imports or duplicating symbols during refactors.
• Minimal diffs instead of rewrites: OmniCoder‑9B often returns small, focused patches rather than rewriting entire files, which is crucial for tools like Claude Code, OpenCode, or VS Code agents that apply patches automatically.
• Error recovery: The model was exposed to many failure–fix cycles, so it is trained to pay attention to compiler diagnostics, LSP errors, and failing tests, then trace issues back to root causes rather than just patch the last visible error.​
• Thinking mode: OmniCoder supports explicit <think>…</think> reasoning segments, where it performs multi‑step planning before emitting final edits or code, similar to “chain of thought” modes in frontier APIs.​

Community feedback from r/LocalLLaMA and a Hacker News discussion of LocalAgent v0.5.0 notes that the OmniCoder‑9B Q8_0 quantization is one of the few small local models that remains stable in tightly constrained evaluation‑gated agent workflows, avoiding fake progress and staying on task in real repositories.

2. Hardware requirements, quantizations, and deployment options

2.1 Quantized GGUF variants and footprint

Tesslate publishes a full GGUF suite for OmniCoder‑9B, with quantizations from 2‑bit to bf16 and clearly documented approximate file sizes. These are designed for llama.cpp, LM Studio, and other GGUF‑compatible runtimes.​

​

These sizes make OmniCoder‑9B accessible on 8 GB consumer GPUs in Q3–Q4 quantization, and on 16–24 GB cards in higher‑precision formats.​​

2.2 VRAM requirements and example setups

Documentation and third‑party hardware analysis for Qwen3.5‑9B indicate that full‑precision (bf16) inference requires roughly 18 GB of VRAM, while a 4‑bit quantized variant needs around 5 GB with additional memory for the key–value cache, especially at long context lengths.

OmniCoder‑9B runs via a VLLM‑like stack on an RTX 6000 48 GB GPU with the model consuming about 44 GB VRAM at the full recommended 262K context, and suggestion is to reduce the context to 8–16K tokens on smaller cards.​​

A Reddit user reports running the Q4_K_M GGUF build with llama.cpp and an OpenCode‑style agent on an 8 GB card at a 100K context window, achieving about 40 tokens per second (TPS) and stable behavior across multiple coding tasks.

The Ollama model page confirms that the omnicoder-9b:q4_k_m tag weighs about 5.7 GB and exposes the full 256K context window; a higher‑quality q8_0 variant is available at around 9.5 GB.

In practice:

• Laptop or small desktop (8 GB VRAM): Use Q3_K_M or Q4_K_M with a context window of 16–64K for interactive coding help and small agents.
• Mid‑range workstation (12–24 GB VRAM, e.g., RTX 4070/4080/4090 or A5000): Run Q4_K_M or Q5_K_M at 64–128K context; consider Q8_0 for higher‑fidelity benchmarks.
• High‑end or cloud GPU (A6000, L40S, A100, H100): Run bf16 or Q8_0 at 262K context for long‑horizon multi‑repo agents.

2.3 Cloud GPU pricing (for local deployments in the cloud)

Because OmniCoder‑9B is fully open‑weight, the main ongoing cost is hardware. Price comparison tools show that an RTX A6000 48 GB – a natural fit for bf16 OmniCoder with a large context – rents in 2026 for roughly 0.27–0.50 USD per GPU‑hour on decentralized or specialist cloud providers, and about 0.33 USD per hour on RunPod. Dedicated documentation for Fluence notes A6000 pricing from 0.32 to 0.98 USD per hour, with many offers clustering around 0.40–0.60 USD per hour and no egress fees.

In other words, running a bf16 OmniCoder‑9B instance for an entire workday on an A6000 can cost on the order of 3–5 USD, while Q4_K_M on a smaller A5000 or RTX 4090 can be significantly cheaper. This is often less expensive than paying per‑token for premium hosted coding models, especially for heavy internal usage.

2.4 Supported runtimes

The OmniCoder‑9B ecosystem is unusually rich at launch:

• Hugging Face Transformers: Official AutoModelForCausalLM and AutoTokenizer quickstart for direct Python use.​
• vLLM: Official vllm serve Tesslate/OmniCoder-9B command with OpenAI‑compatible HTTP endpoint.​
• llama.cpp (GGUF): Official command snippets via llama-cli and llama-server using GGUF files from Tesslate/OmniCoder‑9B‑GGUF.​
• Ollama: A curated model card (carstenuhlig/omnicoder-9b) that exposes a 256K context window and variants latest/q4_k_m (5.7 GB) and q8_0 (9.5 GB).​
• Desktop UIs: Tools like LM Studio and Open WebUI can consume the GGUF or vLLM endpoints; a YouTube tutorial demonstrates OmniCoder‑9B running via Open WebUI with recommended decoding settings.​​

This diversity makes it straightforward to integrate OmniCoder‑9B into IDE extensions, local agents, and custom dashboards.

3. Installing OmniCoder‑9B locally: step‑by‑step

3.1 Installing with Hugging Face Transformers

For users comfortable with Python, the vanilla Transformers path offers maximum control.

1. Set up a Python environment. Install Python 3.10+ and create a virtual environment, then pip install transformers accelerate torch (or your preferred CUDA build).
2. Load the model and tokenizer. The official model card provides a minimal chat example:

pythonfrom transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Tesslate/OmniCoder-9B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)

messages = [
{"role": "system", "content": "You are a helpful coding assistant."},
{"role": "user", "content": "Write a Python function to find the longest common subsequence of two strings."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(
**inputs,
max_new_tokens=2048,
temperature=0.6,
top_p=0.95,
top_k=20,
)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

3. Use recommended sampling parameters. Tesslate suggests temperature 0.6, top‑p 0.95, top‑k 20, and presence penalty 0 for general coding chat, with lower temperatures (0.2–0.4) for deterministic agentic workflows.​
4. Optimize for your GPU. If VRAM is tight, enable 4‑bit quantization via bitsandbytes or load a smaller LoRA‑merged checkpoint once such variants are published.

3.2 OmniCoder‑9B with vLLM (OpenAI‑compatible API)

vLLM is ideal when other services (like IDE plugins or custom agents) expect an OpenAI‑style HTTP API.

1. Install vLLM. On a CUDA‑equipped machine, run pip install vllm or follow the official vLLM installation docs.
2. Start the server:

bashvllm serve Tesslate/OmniCoder-9B \
--tensor-parallel-size 1 \
--max-model-len 65536

You can adjust --max-model-len downwards if VRAM is limited; for example, 8192 or 16384 tokens on an 8–12 GB GPU.

3. Call the model via OpenAI client libraries:

pythonfrom openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="token")

resp = client.chat.completions.create(
model="Tesslate/OmniCoder-9B",
messages=[
{"role": "user", "content": "Explain the difference between a mutex and a semaphore."},
],
temperature=0.6,
)
print(resp.choices[0].message.content)

This setup lets any OpenAI‑compatible client, including many editors and orchestration frameworks, talk to OmniCoder‑9B simply by changing the base URL and model name.

3.3 Running GGUF builds with llama.cpp

llama.cpp is a C/C++ inference engine optimized for CPU and GPU, and GGUF is its native format. Tesslate exposes OmniCoder‑9B GGUF files on Hugging Face under Tesslate/OmniCoder-9B-GGUF.​

1. Install llama.cpp. On macOS with Homebrew, brew install llama.cpp; on Linux or Windows, clone the GitHub repo and run cmake/make according to upstream instructions.​
2. Run interactive chat:

bashllama-cli \
--hf-repo Tesslate/OmniCoder-9B-GGUF \
--hf-file omnicoder-9b-q4_k_m.gguf \
-p "Your prompt" \
-c 8192

3. Expose an OpenAI‑compatible server:

bashllama-server \
--hf-repo Tesslate/OmniCoder-9B-GGUF \
--hf-file omnicoder-9b-q4_k_m.gguf \
-c 8192

4. Tuning for performance:

• Start with -c 8192 on 8 GB GPUs; increase context length only after confirming headroom.​​
• Use the recommended Q4_K_M quantization for most users; switch to Q5 or Q8 on 16+ GB VRAM for higher quality.​
• Enable GPU offloading with --n-gpu-layers or equivalent flags where available.

3.4 Installing via Ollama for a one‑command setup

Ollama offers perhaps the easiest setup path on macOS, Windows, and Linux.

1. Install Ollama from the official site, then open a terminal.
2. Pull and run OmniCoder‑9B:

bashollama run carstenuhlig/omnicoder-9b

The Ollama card lists three tags: latest and q4_k_m at 5.7 GB with a 256K context window, and q8_0 at 9.5 GB, all configured as text‑only models.

Once pulled, the model can be used interactively in the terminal, programmatically via the Ollama HTTP API, or as a backend for coding agents like Claude Code and OpenCode using integration commands such as ollama launch claude --model carstenuhlig/omnicoder-9b.​

Community notes on the Ollama card mention that Q8_0 on dual RTX 5060 Ti GPUs matched a 30B mixture‑of‑experts model on at least one FastAPI refactoring task while maintaining clean diffs and handling async versus sync database sessions correctly, with roughly 3000 prompt tokens per second during evaluation.​

3.5 Desktop UIs: LM Studio and Open WebUI

Many users prefer a graphical interface for quick experiments. GGUF support in tools like LM Studio means OmniCoder‑9B can be added from the Hugging Face model list, after which the UI handles downloading and llama.cpp configuration automatically.

4. Benchmarks and quick comparison

4.1 Official OmniCoder‑9B benchmarks

Tesslate reports several headline benchmarks for OmniCoder‑9B, focusing on reasoning and tool‑use‑heavy tasks rather than pure code‑completion suites.​

Headline takeaways:

• OmniCoder‑9B improves GPQA Diamond pass@1 by about 2.1 points over Qwen3.5‑9B base (83.8 versus 81.7), which is significant on a difficult graduate‑level reasoning benchmark.
• It scores 90 percent pass@5 on AIME 2025, close to what much larger reasoning models achieve, suggesting strong math and problem‑solving capabilities.​
• On Terminal‑Bench 2.0, which evaluates multi‑step shell tasks, OmniCoder‑9B reaches 23.6 percent, about 61 percent higher than the Qwen3.5‑9B base model at 14.6 percent, though still behind larger GLM‑4.7 variants.​

These numbers are self‑reported and should be validated independently, but they align with anecdotal reports that the model “punches above its weight” relative to its parameter count.​​

Pricing

OmniCoder‑9B itself is free to use under Apache 2.0; your only cost is hardware.

In 2026, RTX A6000 48 GB rentals start around 0.27–0.50 USD/hour on specialist and decentralized clouds, and about 0.33 USD/hour on RunPod, making a full 8‑hour workday of bf16 OmniCoder‑9B inference roughly 3–5 USD.

For smaller Q4_K_M quantizations (~5.7 GB), cheaper GPUs such as A5000 or RTX 4090 can be used from roughly 0.11–0.20 USD/hour on many providers.

4.2 Quick capabilities comparison with key competitors

To put OmniCoder‑9B in context, it helps to compare it to both its base model and a few popular long‑context or coding‑oriented alternatives.

4.3 OmniCoder‑9B’s unique selling points (USP)

From the perspective of a local developer or tooling builder, OmniCoder‑9B’s USPs are:

• Frontier‑grade agent behaviors in a 9B model. It explicitly replicates patterns from Claude Opus, GPT‑5.x Codex, and Gemini coding agents, including read‑before‑write, root‑cause analysis, and diff‑oriented edits.​
• Long context at a small size. It keeps Qwen3.5’s 262K native context window, which is unusually large for a 9B model and helpful for multi‑file or even multi‑repo tasks.
• Local‑first deployment. Full Apache 2.0 licensing, GGUF quantizations down to ~3.8 GB, and first‑class support in llama.cpp, vLLM, and Ollama make it straightforward to run entirely on‑prem or on personal hardware.​
• Competitive reasoning benchmarks. GPQA Diamond and AIME 2025 scores comparable to or better than much larger models indicate that the agentic training pipeline successfully distills reasoning strategies, not only code syntax.

5. Designing realistic demos and tests for OmniCoder‑9B

5.1 Simple interactive demos

For an initial demo, start with tasks that show off the model’s agentic editing habits rather than just raw completion.

Example 1 – Bug‑fixing in a Python project:

1. Provide the contents of a file and the traceback from a failing unit test.
2. Ask OmniCoder‑9B to identify the root cause and propose a minimal diff, not a full file rewrite.
3. Apply the diff and re‑run tests; iterate by pasting new failures.

Example prompt (within <think> mode):

You are a senior Python engineer. Read the existing code and the failing traceback carefully before writing. Explain the root cause briefly, then propose a minimal patch as a unified diff.

Here is the file:

... your code ...

Example prompt (within <think> mode):

text<think>
You are a senior Python engineer. Read the existing code and the failing traceback carefully before writing.
Explain the root cause briefly, then propose a minimal patch as a unified diff.
</think>

Here is the file:

```python
... your code ...

Here is the failing test output:

text... pytest traceback ...
text
This plays directly to the model’s strengths around read‑before‑write and diff‑style edits.

Example 2 – Fast front‑end prototype:

The Demo uses OmniCoder‑9B to generate a self‑contained HTML/JavaScript “booster rocket” mini‑game, with controls and canvas rendering logic. A similar demo prompt could describe a small interactive tool (e.g., a kanban board, markdown editor, or visualizer), then ask OmniCoder to produce a single HTML file with embedded CSS/JS and comments.

This kind of task showcases the model’s ability to plan components (layout, state management, event handlers), implement them, and correct mistakes after manual feedback.

5.2 Building a minimal benchmarking harness

To compare OmniCoder‑9B to other local models on your own hardware, consider three dimensions:

• Answer quality on a fixed set of coding challenges.
• Latency and throughput (tokens per second, time to first token).
• Robustness in multi‑step agent loops.

A simple local benchmark workflow could look like this:

1. Select tasks. Choose 20–50 problems from familiar benchmarks like HumanEval or MBPP, or create your own mix of bug‑fixing, refactoring, and “write a small app” tasks.
2. Standardize prompts. For each task, craft a clear prompt with input–output specifications and instructions to think first and then output only the final answer or diff.
3. Script the runs. Use a Python script that sends each prompt to different models via OpenAI‑compatible APIs (vLLM, llama‑server, Ollama) and records outputs and latency.
4. Score results. For function‑writing tasks, use unit tests to score pass/fail automatically. For refactors and bug‑fixing, manually review diffs or measure test suite outcomes.
5. Track metrics. Log per‑task token counts (prompt and completion), TPS, and wall‑clock times.

Even without reproducing official GPQA or AIME setups, such a harness gives a realistic picture of how different models behave in your actual workflow.

5.3 Agent‑loop testing

Because OmniCoder‑9B is built for multi‑step agents, it is important to test it in that context, not only as a chat model.

Recommended tests:

• Repository copilots: Integrate OmniCoder‑9B as the coding backend in tools like OpenCode or Claude Code, then run it on medium‑sized repositories (e.g., a FastAPI backend or React front‑end) with tasks like “migrate to async SQLAlchemy” or “extract this feature into a library.”
• Terminal agents: Combine the model with a sandboxed shell and test tasks like “set up a new Python package with linting and CI” or “optimize this Dockerfile.” Terminal‑Bench 2.0 scores suggest it is significantly more competent than Qwen3.5‑9B in such tasks, though still behind larger GLM‑4.7 variants.
• Error‑recovery loops: Deliberately introduce compile‑time and runtime errors, then ask the agent to fix them. Track how often it identifies the root cause versus patching symptoms.

Evaluating these loops can reveal qualitative differences between models that raw benchmarks may miss.

6. How OmniCoder‑9B compares to other coding models

6.1 Versus Qwen3.5‑9B base

Qwen3.5‑9B is a strong generalist foundation model with excellent performance on broad benchmarks such as MMLU‑Pro and LiveCodeBench, multilingual support across more than 200 languages, and multimodal capabilities, all while maintaining the same 262K native context window.

However, its default behavior in coding tasks is that of a typical LLM: it often rewrites entire files, sometimes ignores diagnostics, and may not follow strict diff formats unless meticulously prompted.

OmniCoder‑9B, by contrast, systematically improves GPQA and Terminal‑Bench performance and is tuned for agentic coding behaviors out of the box.

The trade‑off is a narrower training focus: the authors note weaker performance in niche languages such as Haskell, MATLAB, and assembly, and more limited general knowledge coverage due to the dataset’s Python/JavaScript skew.

6.2 Versus larger open‑weight models (GPT‑OSS‑20B, GLM‑4.7‑Flash)

Open‑weight long‑context reasoning models like GPT‑OSS‑20B and GLM‑4.7‑Flash offer impressive benchmark numbers and, in GLM‑4.7‑Flash’s case, leading scores on several reasoning and chat tasks, while still fitting within 24 GB VRAM. For pure math or multi‑domain reasoning, these larger or more specialized models may outperform OmniCoder‑9B.

However, GPT‑OSS‑20B requires roughly double the parameters, making local deployment notably more expensive in terms of VRAM and throughput, while GLM‑4.7‑Flash—though relatively small—tends to be served through vendor‑hosted APIs and is not as heavily optimized for repository‑scale coding diffs. OmniCoder‑9B occupies a sweet spot where it is small enough for consumer GPUs yet tuned explicitly for coding agents.

6.3 Versus proprietary hosted coding agents (Claude Haiku 4.5, etc.)

Anthropic’s Claude Haiku 4.5 Thinking model brings extended reasoning, computer‑use (GUI interaction), and 200K context to a low‑latency API with pricing around 1 USD per million input tokens and 5 USD per million output tokens, plus thinking‑token surcharges.

In hosted IDE integrations, it can act as a powerful coding copilot with fine‑tuned behaviors, but it cannot be self‑hosted and costs accumulate quickly for heavy internal workloads.

By contrast, OmniCoder‑9B has a one‑time download cost and can then be hosted indefinitely on local or rented hardware, with marginal costs driven solely by GPU hours.

For companies or teams that already rent A6000‑class GPUs in the 0.30–0.60 USD per hour range, running OmniCoder instead of paying per token for every coding session can be significantly cheaper at scale, especially when serving many developers.

7. Best practices for running and tuning OmniCoder‑9B locally

7.1 Recommended decoding settings

Based on Tesslate’s guidance and initial community experiments, sensible defaults are:

• Temperature: 0.6 for interactive coding chat; 0.2–0.4 for strict agents or when reproducibility is critical.
• Top‑p: 0.95 and top‑k: 20, which balance creativity and determinism in code output.[1]
• Presence penalty: 0.0 by default; raise slightly (0.2–0.4) if the model tends to repeat itself in long sessions.

Agents can also explicitly separate reasoning and action phases by placing planning instructions inside <think> tags and asking the model to emit code or diffs only outside those tags.

7.2 Prompting patterns for code editing

Effective patterns include:

• Diff‑oriented prompts: “Read the existing file, explain the bug briefly, then output a unified diff patch only.” This encourages minimal changes aligned with the model’s training.
• Compiler‑feedback loops: “Here is the compiler error. Do not write new code from scratch; fix the underlying bug causing this error.” This aligns with its error‑recovery traces.
• Multi‑file context: Use the long context window to include the main file, its dependencies, and relevant configuration (e.g., package.json, Dockerfile) so that OmniCoder can reason holistically about changes.

7.3 Operational considerations

• Logging and replay: Because OmniCoder‑9B is trained on trajectories, it is natural to capture and replay your own agent traces (prompts, tool calls, diffs, tests) for future fine‑tuning or evaluation.
• Safety and sandboxing: When combining the model with a shell or file‑system tools, run in a sandbox (containers, firejail, or restricted VMs) to avoid destructive actions.
• Monitoring performance: Track tokens per second, memory usage, and error rates to decide when to move from Q4 to Q8 or bf16, or when to scale across multiple GPUs using tensor or pipeline parallelism.

8. Conclusion

OmniCoder‑9B occupies a compelling niche in the 2026 local‑AI landscape: a compact, long‑context, Apache‑licensed coding agent that incorporates behaviors distilled from frontier proprietary models and delivers benchmark results competitive with much larger systems.

FAQs

1. What is OmniCoder‑9B and why is it special?
OmniCoder‑9B is a 9B‑parameter coding agent fine‑tuned on 425K real agentic coding trajectories from models like Claude Opus and GPT‑5.x, focusing on read‑before‑write, diff‑style edits, and error recovery instead of naive code completion.​

2. What hardware do I need to run OmniCoder‑9B locally?
With Q4_K_M (~5.7 GB) you can run OmniCoder‑9B on an 8 GB GPU at moderate context (16–64K tokens); higher‑precision Q8_0 or bf16 typically need 16–48 GB VRAM, especially for the full 262K context window.

3. How do I install OmniCoder‑9B the easiest way?
For most users, the fastest path is ollama run carstenuhlig/omnicoder-9b, which downloads a 5.7 GB Q4_K_M build with a 256K context window and exposes it via the Ollama CLI and HTTP API.​

4. How does OmniCoder‑9B compare to larger models?
On GPQA Diamond and AIME 2025, OmniCoder‑9B matches or beats several much larger long‑context models while being small enough for consumer‑grade GPUs, thanks to its agentic training on curated coding trajectories.​​

5. Can I use OmniCoder‑9B in commercial projects?
Yes. The model is released under the Apache 2.0 license, so you can integrate it into commercial tools and services as long as you comply with standard attribution and notice requirements.​