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Can you run Mistral Large 2411 locally?

Mistral family · · 122.6B parameters · Hugging Face model card

Mistral Large 2411 is a 122.6B-parameter model open-weight model with a 131,072-token context window, usable locally with roughly 96 GB of memory at Q4-class quantization (calculated; measured speeds vary by hardware).

Minimum: 96 GB+ memory (Q4_K_M, calculated)  ·  Recommended: 96 GB+ memory for full context (Q4_K_M, calculated)

Parameters (total)
122.6B
VRAM at Q4_K_M (4k ctx)
82.4 GB
Context window
131,072 tokens
Architecture source
HF config.json (verified)

How much VRAM does Mistral Large 2411 need at each quantization?

Mistral Large 2411 needs 82.4 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.

QuantizationBits / weightWeights onlyTotal + KV @4k ctxTotal + KV @32k ctx
Q4_K_M 4.8 73.6 GB 82.4 GB 92.7 GB
Q5_K_M 5.7 87.4 GB 97.6 GB 107.9 GB
Q6_K 6.6 101.2 GB 112.8 GB 123.1 GB
Q8_0 8.5 130.3 GB 144.8 GB 155.1 GB
FP16 16 245.2 GB 271.2 GB 281.6 GB

Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (88 layers, 8 KV heads, 128 head dim). Source: HF-verified 2026-09-12 via api (params=122.61B ctx=131072 license=other); VRAM figures are documented calculations, not measurements

Which GPUs can run Mistral Large 2411 locally?

At Q4_K_M with a 4k context, Mistral Large 2411 needs 82.4 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.

Runs Mistral Large 2411 comfortably (25%+ VRAM headroom)

GPUVRAMBandwidthType
H200 SXM 141 GB 4800 GB/s Data Center GPU
AMD Instinct MI300X 192 GB 5300 GB/s Data Center GPU
NVIDIA B200 192 GB 8000 GB/s Data Center GPU
AMD Instinct MI325X 256 GB 6000 GB/s Data Center GPU
AMD Instinct MI355X 288 GB 8000 GB/s Data Center GPU
NVIDIA B300 (Blackwell Ultra) 288 GB 8000 GB/s Data Center GPU

Minimum GPUs that fit Mistral Large 2411 at Q4_K_M

These GPUs hold the model but leave little headroom — keep contexts short.

GPUVRAMBandwidthType
NVIDIA RTX PRO 6000 Blackwell 96 GB 1792 GB/s Pro GPU Check price

Needs 2+ GPUs to run Mistral Large 2411

One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 82.4 GB requirement. See our multi-GPU guide for setup.

GPUVRAMBandwidthType
AMD Radeon Pro W7900 48 GB ×2 864 GB/s Pro GPU Check price
NVIDIA RTX PRO 5000 Blackwell 48 GB ×2 1344 GB/s Pro GPU
RTX 6000 Ada 48 GB ×2 960 GB/s Pro GPU Check price
RTX A6000 48 GB ×2 768 GB/s Pro GPU Check price
H100 SXM 80 GB ×2 3350 GB/s Data Center GPU
NVIDIA A100 80GB SXM 80 GB ×2 2039 GB/s Data Center GPU
NVIDIA H100 PCIe 80GB 80 GB ×2 2039 GB/s Data Center GPU

Can a Mac run Mistral Large 2411?

Yes — these Apple Silicon machines fit Mistral Large 2411 at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.

Apple SiliconUnified memoryUsable by GPU (~75%)Bandwidth
Apple M3 Max 128 GB 96 GB 400 GB/s
M4 Max (MacBook Pro) 128 GB 96 GB 546 GB/s
M3 Ultra (Mac Studio) 512 GB 384 GB 819 GB/s

Can Mistral Large 2411 run on mini PCs, Jetson, or NPU devices?

These edge and NPU devices from our database have enough memory for Mistral Large 2411 at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.

DeviceMemoryBandwidthType
AMD Ryzen AI 9 HX 370 (Strix Point) 96 GB 120 GB/s NPU Chip

Which pre-built systems can run Mistral Large 2411?

These mini PCs and workstations from our database fit Mistral Large 2411 at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.

SystemTypeMemoryUsable for AIBandwidthGPUs
BOXX APEXX 8R (1x RTX PRO 6000 Blackwell) Workstation 96 GB 96 GB 1792 GB/s 1
GEEKOM A9 Mega AI Workstation Workstation 128 GB 96 GB 1
NOVATECH RTX PRO 6000 AI Workstation Workstation 96 GB 96 GB 1
System76 Thelio Major (1x RTX PRO 6000 Blackwell) Workstation 96 GB 96 GB 1792 GB/s 1
System76 Thelio Major (2x RTX 6000 Ada) Workstation 96 GB 96 GB 960 GB/s 2
Lenovo ThinkStation PGX Workstation 128 GB 115.2 GB 273 GB/s 1
NVIDIA DGX Spark Workstation 128 GB 115.2 GB 273 GB/s 1
BIZON G3000 G2 (4x RTX 5090) Workstation 128 GB 128 GB 1792 GB/s 4
Apple Mac Studio M2 Ultra (192GB) Workstation 192 GB 144 GB 800 GB/s 1
HP Z8 Fury G5 (4x RTX 6000 Ada) Workstation 192 GB 192 GB 960 GB/s 4

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