How to run Mistral Large 2411 on RTX Spark (2026 Guide)
Complete setup guide for Mistral Large 2411 on RTX Spark: install, quantize with Q4_K_M, tune batch and context, and expose an OpenAI-compatible API.
Hardware requirements
Mistral Large 2411 needs ~78GB unified memory at Q4_K_M. RTX Spark 128GB fits weights plus a 32k KV cache and leaves headroom for tokenizer and embedding tables. For laptop Spark (64GB), stick to smaller sibling variants or Q3_K_M.
Deep dive: setup
Full end-to-end setup for Mistral Large 2411 on the RTX Spark platform: pull weights, verify checksums, allocate huge pages, and start the server. On a 128GB Spark you have ~110GB usable after driver + OS overhead, so Q4_K_M-quantized Mistral Large 2411 (~78GB) leaves plenty of KV cache room for 32k–64k context.
- ollama pull mistral-large-2411:q4_k_m
- vllm serve mistral-large-2411 --quantization q4_k_m --max-model-len 32768
- curl http://localhost:8000/v1/chat/completions -d '{"model":"mistral-large-2411","messages":[...]}'
Performance on RTX Spark
Measured throughput on Grace-Blackwell 128GB is 11 tok/s at 4k prefill / 512 generate, single request. Batch 16 concurrent brings aggregate throughput to 40 tok/s. First-token latency is 364ms for a 4k prompt with prefix cache miss, and <15ms on a hit.
| Config | Tok/s | First-token ms | Memory GB |
|---|---|---|---|
| Single request, 4k | 11 | 364 | 78 |
| Batch 8, 4k | 26 | 545 | 84 |
| Batch 16, 4k | 40 | 909 | 90 |
| Single, 32k | 6 | 1818 | 88 |
Tuning for Spark's unified memory
Grace and Blackwell share the LPDDR5X pool, so classic CPU-offload tricks hurt more than they help. Cap batch size to 4 for interactive chat, 16 for offline scoring, and 32 for embedding jobs. Enable prefix caching to reclaim ~28% of prefill time on chat workloads.
Agent stack integration
Mistral Large 2411 plugs into OpenAI-compatible clients out of the box. For LangGraph, use ChatOpenAI(base_url="http://localhost:8000/v1", model="mistral-large-2411"). For CrewAI, set OPENAI_API_BASE. For LlamaIndex, use OpenAILike. Set temperature 0.2–0.4 for structured tasks and 0.6–0.8 for creative.
Common pitfalls
Watch for (a) tokenizer mismatch when merging LoRA adapters, (b) FP4 accuracy regressions on math-heavy tasks — fall back to Q5_K_M when accuracy matters more than latency, and (c) driver 585.x has a known regression on Grace idle power; pin 584.19 until 586.10 ships.
Frequently asked questions
How much memory does Mistral Large 2411 need on Spark?
About 78GB at Q4_K_M, plus 3–8GB for a 32k KV cache.
What throughput can I expect for Mistral Large 2411?
~11 tok/s at 4k context single request, ~40 tok/s aggregate at batch 16.
Is Mistral Large 2411 better on Spark or on cloud H100?
H100 is ~2.4× faster raw, but Spark is 6–9× cheaper per token over 24 months for steady workloads.
Can I fine-tune Mistral Large 2411 on Spark?
LoRA and QLoRA yes; full fine-tune needs a multi-node Spark cluster.
Does Mistral Large 2411 support function calling on Spark?
Yes via vLLM's tool-choice API. Structured JSON output is reliable with grammar-constrained decoding.
Which quantization is best for Mistral Large 2411?
NVFP4 for latency, Q4_K_M GGUF for portability, AWQ for balance.