DeepSeek R1 Distill Mistral 24B
Community R1 distill onto a Mistral Small 3 base. Apache 2.0; combines R1 reasoning with Mistral instruction polish.
Positioning
DeepSeek R1 Distill Mistral 24B is a community-built model that distills the reasoning capabilities of DeepSeek R1 onto a Mistral Small 3 base. Released under Apache 2.0, it combines the structured chain-of-thought reasoning from the R1 lineage with the polished instruction-following of Mistral. As a dense 24B-parameter model with a 32K context window, it targets consumer-tier hardware while offering a permissive license for commercial use.
Strengths
- Permissive Apache 2.0 license: Unlike many reasoning-focused models that carry restrictive licenses, this model is fully open for commercial deployment, modification, and redistribution.
- Dense architecture with 24B parameters: Inference cost is predictable and scales linearly with parameter count, unlike MoE models where active vs. total params can complicate resource planning.
- 32K context window: Sufficient for multi-turn conversations, document analysis, and tasks requiring moderate-length reasoning chains.
- Combines R1 reasoning with Mistril instruction polish: Inherits the structured reasoning approach of DeepSeek R1 while benefiting from Mistral's well-tuned instruction-following base.
Limitations
- No community-reported benchmarks available: Operators should treat any vendor-published metrics as best-case until independent measurements emerge.
- 24B dense parameters require significant VRAM: Even at Q4_K_M (~13.5 GB), the model plus KV cache and overhead can exceed 20 GB, pushing the limits of consumer GPUs with 16 GB VRAM.
- Distillation may reduce reasoning depth: As a distilled variant, it may not match the full R1 model's performance on complex multi-step reasoning tasks.
- Community-maintained: Unlike vendor-supported models, updates and support rely on community contributions, which may be less consistent.
What it takes to run this locally
At FP16, the model requires 48 GB of disk space and roughly 48 GB of VRAM, placing it in workstation or datacenter territory. Quantized versions reduce the footprint: Q8_0 (26 GB), Q6_K (19.8 GB), Q5_K_M (17.1 GB), Q4_K_M (13.5 GB), Q3_K_M (11.7 GB), and Q2_K (7.8 GB). For typical use with a 32K context, add 30–50% for KV cache and framework overhead. A Q4_K_M quant (13.5 GB) plus overhead may fit on a single 24 GB consumer GPU (e.g., RTX 4090), while Q2_K (~7.8 GB) could run on a 12–16 GB card with reduced quality.
Should you run this locally?
Yes if you need a permissive Apache 2.0 license for commercial reasoning tasks and have a GPU with at least 16 GB VRAM (for Q4_K_M or lower quants). It is a strong choice for developers who want to deploy a reasoning model without licensing restrictions.
No if you require the full reasoning depth of the original DeepSeek R1, or if your hardware is limited to 12 GB VRAM or less, as even Q2_K may struggle with the 32K context overhead. Also consider waiting for community benchmarks to validate real-world performance.
Catalog cross-links
- DeepSeek R1
- Mistral Small 3
- Consumer GPU Guide
Overview
Community R1 distill onto a Mistral Small 3 base. Apache 2.0; combines R1 reasoning with Mistral instruction polish.
How to run it
DeepSeek R1 Distill Mistral 24B is a reasoning-distilled model based on Mistral 24B, trained on DeepSeek-R1's chain-of-thought reasoning traces. Same distillation approach as the Qwen variant but on Mistral architecture. Run at Q4_K_M via Ollama (ollama pull deepseek-r1:24b-mistral) or llama.cpp with -ngl 999 -fa -c 16384. Q4_K_M file size ~14 GB on disk. Minimum VRAM: 12 GB — RTX 4070 (12GB) at Q4_K_M with KV offload for 4K context. RTX 4090 24GB: Q4_K_M comfortably at 16K+ context. Recommended: RTX 4090 24GB at Q4_K_M. Throughput: ~40-65 tok/s on RTX 4090 at Q4_K_M. Mistral architecture — well-supported. Key characteristic: includes <think> CoT blocks before answers. Generation is 2-4× longer than standard Mistral 24B for the same prompt. Budget max_tokens accordingly. Use for: complex reasoning, math, logic, multi-step problem solving. Not for: quick factual lookups, simple classification — CoT overhead isn't justified. The Mistral-based distill may have different reasoning characteristics than the Qwen-based distill — test both for your use case. For Qwen-based distill: DeepSeek R1 Distill Qwen 3 32B. Context: Mistral's 32K+; practical at Q4 on 24 GB is 16-32K.
Hardware guidance
Minimum: RTX 3060 12GB at Q3_K_M with KV offload. Recommended: RTX 4090 24GB at Q4_K_M (16K+ context). VRAM math: 24B dense, Q4_K_M ≈ 14 GB. KV cache at 16K: ~5 GB. Total: ~19 GB at 16K. RTX 4090 24GB: comfortable on-GPU. RTX 3080 10GB: Q3_K_M with KV offload. RTX 4080 16GB: Q4 + 8K context on-GPU. MacBook Pro M4 Pro 24GB+: Q4 at 15-30 tok/s. Cloud: A10 24GB at Q4_K_M. Budget for 2-4× output tokens vs non-reasoning models. AWQ-INT4 drops to ~12 GB. The smaller model size makes this more accessible than the 32B/70B R1 distill variants — good for resource-constrained reasoning tasks.
What breaks first
- CoT token explosion. Same as all R1 distill models — 200-1000+ extra tokens of reasoning before every answer. Budget max_tokens and latency expectations. 2. Mistral vs Qwen reasoning style. DeepSeek-R1's reasoning traces were distilled onto different base architectures. The Mistral-backed distill may reason differently than the Qwen-backed one. Test which fits your tasks better. 3.
<think>tag parsing. Outputs contain<think>...</think>blocks. Must be parsed or stripped before user display. 4. Q3 reasoning chain quality. At Q3, intermediate reasoning steps degrade — logical errors compound through CoT. Use Q4_K_M minimum for reasoning tasks.
Runtime recommendation
Common beginner mistakes
Mistake: Setting max_tokens=1024 for reasoning tasks. Fix: CoT adds 200-1000+ tokens before the answer. Set max_tokens=4096+ for complex reasoning. Mistake: Displaying <think> blocks to end users. Fix: Parse and strip <think>...</think> blocks. Optionally show them behind a "show reasoning" toggle. Mistake: Using R1-distill Mistral for simple factual queries. Fix: The CoT overhead isn't worth it. Use standard Mistral 24B for non-reasoning tasks. Mistake: Mixing this with the Qwen-based distill. Fix: Different base architectures, different templates. Keep them separate. Test which performs better on your tasks.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Apache 2.0 reasoning model
- Mistral instruction-following base
Weaknesses
- Community distill — less validated than Qwen / Llama distills
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 14.0 GB | 18 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of DeepSeek R1 Distill Mistral 24B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run DeepSeek R1 Distill Mistral 24B?
Can I use DeepSeek R1 Distill Mistral 24B commercially?
What's the context length of DeepSeek R1 Distill Mistral 24B?
Source: huggingface.co/community/DeepSeek-R1-Distill-Mistral-24B
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify DeepSeek R1 Distill Mistral 24B runs on your specific hardware before committing money.