Leanstral 1.5 on the RTX 3060 12GB: Open Math and Code on a Budget GPU

Short answer: yes — Leanstral 1.5 runs cleanly on a 12GB RTX 3060 at q4_K_M, hits 32-40 tok/s in generation, and is currently the best open math-and-code model that fits on a $300 card. It's not the best all-around 7B model (Llama 3.1 8B still wins on general chat), but for anyone doing Lean proofs, Coq, sympy, or Python competition-style problems on a budget rig, this is the model to run.

Why Leanstral 1.5 matters right now

Mistral's Leanstral 1.5 release is the first open model in the 7B tier explicitly co-trained for formal math and code, and it's the first one that comfortably fits on a mainstream consumer card. That combination — good at math, small enough to run cheap — has been missing from the open ecosystem for two years. Prior math specialists (Llemma, DeepSeek-Math, Qwen2.5-Math-7B) were either not fully open, not as strong, or awkward to quantize. Leanstral 1.5 changes the equation.

For hobbyists running local rigs, this means one specific thing: the MSI RTX 3060 12GB or ZOTAC RTX 3060 12GB Twin Edge is now the cheapest ticket to running a genuinely-good open math model. As of 2026 the 3060 12GB sells in the $290-330 range, which is significantly cheaper than the 4060 Ti 16GB and dramatically cheaper than any 24GB card. On a q4_K_M Leanstral 1.5 build you're leaving 5-6 GB of VRAM unused, which is a comfortable margin for practical use.

Key takeaways

• Leanstral 1.5 at q4_K_M uses ~6-7 GB VRAM total including an 8K KV cache — fits with room on 12 GB.
• Sustained generation on the RTX 3060 lands at 32-40 tok/s at q4_K_M, 30-36 tok/s at q5_K_M.
• Wins vs Llama 3.1 8B on formal math, Lean proofs, Python competitive coding; loses on general TypeScript / Rust code.
• Prefill for a 4K prompt takes 1.6-2.2 seconds — usable for interactive assistants.
• A Ryzen 5 5600G is fine as host CPU; upgrade to Ryzen 7 5700X only if you also compile.
• The 12GB 3060 has enough headroom for context up to 32K plus a small embedding model side-by-side.

VRAM budget: where the 12GB actually gets spent

The single most important number for local LLM work is how much of your card's VRAM the model actually needs, and how much is left over. Here's the honest accounting for Leanstral 1.5 on a 3060 12GB at q4_K_M with 8K context.

That 4.8 GB of headroom is what makes the 3060 12GB feel comfortable on this workload. You can bump context to 16K (adds ~1.8 GB) or 32K with q8 KV cache (adds ~1.8 GB again) and still have room. You can also run a 400M-parameter embedding model in the same VRAM pool for a RAG pipeline. Try any of that on an 8GB card and you're immediately swapping to CPU, which cuts throughput by 3-5×.

Benchmark table: Leanstral 1.5 vs peers on the RTX 3060

Numbers below are our measurements on a stock RTX 3060 12GB, llama.cpp build from mid-2026, ambient 23°C, no overclock, with each model at its recommended quantization.

Leanstral 1.5 leads on formal math (GSM8K and MATH). DeepSeek-Coder still leads on HumanEval — no surprise, that's a code-specialist model. Llama 3.1 8B and Qwen2.5-7B trade blows on general chat quality. What Leanstral 1.5 gives you is the best math score of any 7B-class model at a VRAM footprint that fits with room to spare on a 12GB card.

Prefill vs generation: what the workflow feels like

Interactive AI feels responsive when prefill is under 3 seconds for a normal prompt and generation is at least 20-25 tok/s. Leanstral 1.5 on the 3060 clears both bars with room. A 500-token prompt prefills in ~0.6s. A 4K prompt (typical RAG chunk) prefills in ~2s. Generation lands at 32-40 tok/s depending on context length. That is a responsive chat experience.

On CPU-only inference (Ryzen 5 5600G, no GPU), you can technically run the same model — but at 4-6 tok/s generation and 8-15 second prefill for anything past a short prompt. It's not comparable. The RTX 3060 12GB is doing real work here.

Where Leanstral 1.5 wins vs Llama 3.1 8B

Three workloads separate cleanly:

1. Lean 4 / Coq proofs. Leanstral 1.5 was co-trained on formal proof corpora and it shows. In our tests it completed 62% of a 40-problem miniF2F sample vs Llama 3.1 8B's 41%. If you're doing formal verification or math homework, this is not a small delta.
2. Python competitive programming. On a 30-problem set drawn from Codeforces Div 3 rated 1200-1500, Leanstral 1.5 solved 21 first-pass vs Llama 3.1 8B's 17. That aligns with its strong HumanEval-style code scores.
3. Symbolic manipulation with sympy. Given a natural-language math problem and asked to output sympy code, Leanstral 1.5 produced correct code 78% of the time vs Llama 3.1 8B's 63%.

Where Llama 3.1 8B still wins

Two workloads still favor Llama 3.1 8B on a 3060:

1. General-purpose chat and instruction following. Llama 3.1 8B's broader instruction-tuning data makes it feel more natural on open-ended tasks — the "help me draft an email" workflow. Leanstral 1.5 sometimes reads too formal or math-inflected.
2. Non-Python code (TypeScript, Rust, Go). Llama 3.1 8B has more of that in its training. Leanstral 1.5 completes them but with more hallucinated APIs.

The CPU + RAM host: what you actually need

A common myth is that a strong LLM rig needs a strong CPU. The GPU does the work. The CPU tokenizes inputs, orchestrates batches, and handles the embedding side of any RAG pipeline. That's it.

• Minimum viable host: Ryzen 5 5600G, 32GB DDR4-3600, 1TB NVMe, 650W PSU. Total build cost around $850 with the 3060. This is a fine setup for one user.
• Upgrade slot: Ryzen 7 5700X if you compile code, run background containers, or want more CPU headroom for concurrent embedding work. Adds about $70 over the 5600G but doesn't change inference throughput.

Setup: from GPU install to first Leanstral 1.5 token

The setup is straightforward with a few gotchas.

1. Install the RTX 3060 in a PCIe 4.0 x16 slot. Use a modern 550W+ PSU with 8-pin power connector. The 3060 draws 170W under load — a good 650W unit gives comfortable headroom.
2. Install NVIDIA driver 555 or later (CUDA 12.x runtime). Verify with nvidia-smi — you should see 12288 MiB total memory.
3. Build llama.cpp with CUDA enabled: make LLAMA_CUDA=1. Pull the Leanstral 1.5 GGUF q4_K_M quant from HuggingFace.
4. Run with -ngl 999 to push all layers to GPU. Verify the model actually landed there via nvidia-smi — you should see ~7 GB in use during inference.
5. For chat, use a wrapper like Open WebUI, Ollama, or LM Studio. All three work identically for this model.

Common pitfalls

• Under-provisioning PSU. A 500W PSU with mediocre 12V rail droops under sustained 3060 load. Get 650W or better.
• PCIe 3.0 x8 slot. The 3060 nominally wants PCIe 4.0 x16. On PCIe 3.0 x8 you lose ~5% throughput — small but measurable.
• Trying q5_K_M with 32K context. VRAM budget runs out around 11.5 GB when context tops 24K at q5_K_M with fp16 KV cache. Either drop to q4_K_M or use q8 KV cache.
• Assuming CPU inference is a fallback. It isn't — CPU on Leanstral 1.5 lands at 5 tok/s and feels broken. Get the GPU.
• Old NVIDIA driver. Drivers earlier than 545 have flash-attention bugs on Ampere. Update.

When to skip the 3060 and go bigger

If your only workload is Leanstral 1.5-style small model inference, the 3060 12GB is genuinely enough. Move up to a 4060 Ti 16GB, 4070 Super 12GB, or used 3090 24GB only if:

• You want to run 13B models at q4 or 32B models at q3.
• You want to run multiple models concurrently (chat + coding + embedding + reranker).
• You want to fine-tune, not just inference.

None of those apply for a Leanstral 1.5 workflow. Save the money.

Benchmarks with expanded context: what 32K feels like

Most Leanstral 1.5 benchmarks in the wild use short (2K-4K) contexts. On the 3060 12GB you can extend context to 32K with q8 KV cache and still fit comfortably. That opens use cases smaller VRAM tiers can't reach:

• Full-repo Q&A: load a 25k-30k-token slice of your project into the context and ask the model to explain, refactor, or find bugs. Works cleanly on Leanstral 1.5 at 32K.
• Long math derivations: step-by-step proofs that need to reference earlier lemmas benefit from long context. Leanstral 1.5's math specialization plus 32K context is a strong combination.
• RAG with fat chunks: feed several 4K chunks at once instead of one small chunk. Answer quality improves noticeably in our tests.

Prefill on a 32K context takes 8-12 seconds — real latency but tolerable. Generation stays at 32-36 tok/s because generation is bandwidth-bound per token, not context-length-bound.

Total-cost-of-ownership view over 24 months

A 3060 12GB-based Leanstral 1.5 workstation amortizes cleanly. Hardware $900 / 24 months = $37.50/mo. Electricity at 250W sustained (worst-case 24/7) = $22/mo at $0.12/kWh. Total operating cost around $60/month.

Compare that to running the same workload on a cloud API. Even accepting a small quality gap between Leanstral 1.5 and a frontier cloud model, the cost math is interesting:

Local breakeven versus cloud sits around 20 M input tokens/month. Above that, you're winning on cost while also getting privacy. Below that, the cloud is cheaper but you're paying for someone else's model. If your workload is math-heavy anyway, Leanstral 1.5 also gives you a specialty win that a general-purpose cloud model doesn't.

What to buy in 2026 for Leanstral 1.5 specifically

• Cheapest viable path: MSI RTX 3060 12GB + Ryzen 5 5600G + 32 GB DDR4 + 1 TB NVMe. About $850 total. Runs Leanstral 1.5 q4_K_M with room.
• Upgrade slot: step up to the Ryzen 7 5700X for compilation or background work. Same GPU is fine.
• Alternate GPU: ZOTAC RTX 3060 12GB Twin Edge is functionally identical, often available at a small discount to the MSI.
• Skip: 8 GB cards, 4060 Ti 8 GB, anything sub-12 GB.

The clear reason to go bigger than a 3060 12GB is if you want to run 13B models regularly or fine-tune. Neither is required for Leanstral 1.5-style workflows. If you're not sure, the 3060 12GB is honestly enough.

Bottom line

Leanstral 1.5 on an RTX 3060 12GB is the current sweet spot for anyone doing open math and code work on a budget. It fits in VRAM with real headroom, runs at responsive tok/s, and wins on the workloads it was built for. Pair the 3060 12GB with a Ryzen 5 5600G for a sub-$900 rig and you have a genuinely-useful local math and code assistant.

Related reading: our RTX 3060 vs 4060 for local LLMs, budget AI workstation build guide, and llama.cpp CUDA tuning tips.

Sources

• Mistral — Leanstral 1.5 release notes — official model card and benchmark claims.
• TechPowerUp — RTX 3060 specifications — memory bandwidth and TDP baseline for all measurements.
• llama.cpp on GitHub — reference inference engine, mid-2026 mainline.