Codestral Mamba 7B
By Mistral AI · France
Updated 2026-07-13
Overview
Mistral AI's pure Mamba SSM architecture for code, with linear-time inference and a 256k context window. Apache 2.0, but tooling support is still patchy.
When to pick this model
- Long-context code analysis across entire repositories
- Research into state-space models for code
- Inference workloads where constant memory matters more than raw quality
- Settings where mistral-inference or vLLM is already in the stack
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 14 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Codestral Mamba 7B fits an 8 GB consumer card at Q4_K_M (5 GB). Stepping up to Q8_0 nearly doubles the footprint to 9 GB, and unquantized FP16 weights take 14 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Codestral Mamba 7B needs roughly 8 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 15 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Codestral Mamba 7B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Codestral Mamba 7B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (14 GB used) |
| 32 GB | RTX 5090 | FP16 (14 GB used) |
Which GPU should you buy to run Codestral Mamba 7B?
To run Codestral Mamba 7B locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Verified 256k context for whole-repo reasoning
- Constant memory footprint regardless of sequence length
- Apache 2.0 license
- Linear-time inference scales gracefully on long inputs
Limitations
- No official Ollama support
- Only partial llama.cpp integration
- Requires mistral-inference or vLLM for full functionality
- Quality trails transformer-based coders of similar size
Typical workloads
In our catalog grid, Codestral Mamba 7B is filed under Long Context Code, Linear Inference — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); French-language output where quality matters.
The 250k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Pure Mamba2 SSM · linear inference
Training: First serious Mamba for code.
The first serious Mamba code model — pick it for long-context experiments, not for daily completion work.
Quick start
# HuggingFace : mistralai/Mamba-Codestral-7B-v0.1Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Codestral Mamba 7B need?
At the recommended Q4_K_M quantization, Codestral Mamba 7B needs about 5 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 14 GB.
Can Codestral Mamba 7B run without a GPU?
Yes — with roughly 8 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Codestral Mamba 7B support?
Codestral Mamba 7B supports a 250k-token context window (256,000 tokens).
Can I use Codestral Mamba 7B commercially?
Yes. Codestral Mamba 7B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Codestral Mamba 7B on consumer hardware?
Our compatibility engine estimates on the order of 40 tokens/sec on a mid-range GPU and up to 100 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Codestral Mamba 7B should I download first?
Start with Q4_K_M (5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.