Mistral Large 2411 (123B) — VRAM Requirements, Quantization Matrix & GPU Guide
Mistral Large 2411 (123B) (Mistral, Mistral Research) is a Dense Transformer architecture with 123B resident parameters (123B active per token), 88 layers, and Grouped-Query Attention (GQA) supporting up to 131,072 context tokens.
Interactive Mistral Large 2411 (123B) Memory & Context Simulator
Precision & Quantization VRAM Matrix (8K / 32K / 128K Context)
| Precision / Format | Effective bpw | Weights Only | Total @ 8K ctx | Total @ 32K ctx | Total @ 128K ctx | Recommended Tier (@ 32K) |
|---|---|---|---|---|---|---|
| FP16 / BF16 | 16.00 bpw | 229.11 GB | 240.37 GB | 248.62 GB | 281.62 GB | Mac Studio 512GB |
| FP8 | 8.50 bpw | 121.71 GB | 129.75 GB | 138.00 GB | 171.00 GB | Mac Studio 192GB |
| Q8_0 | 8.50 bpw | 121.71 GB | 129.75 GB | 138.00 GB | 171.00 GB | Mac Studio 192GB |
| Q6_K | 6.56 bpw | 93.93 GB | 101.14 GB | 109.39 GB | 142.39 GB | NVIDIA H200 141GB |
| Q5_K_M | 5.69 bpw | 81.48 GB | 88.31 GB | 96.56 GB | 129.56 GB | Mac Studio 128GB |
| Q4_K_M | 4.85 bpw | 69.45 GB | 75.92 GB | 84.17 GB | 117.17 GB | RTX PRO 6000 96GB |
| AWQ INT4 | 4.50 bpw | 64.44 GB | 70.76 GB | 79.01 GB | 112.01 GB | RTX PRO 6000 96GB |
| IQ4_XS | 4.25 bpw | 60.86 GB | 67.07 GB | 75.32 GB | 108.32 GB | NVIDIA A100 80GB |
Hardware Compatibility & Decode Speed (Mistral Large 2411 (123B) @ Q4_K_M, 32K Context)
| Hardware Tier | Memory & Bandwidth | Required (32K) | Fit Verdict | Est. Decode Speed |
|---|---|---|---|---|
| RTX 3090 24GB | 24 GB · 936 GB/s | 84.17 GB (351%) | Out of Memory (OOM) | — |
| RTX 4090 24GB | 24 GB · 1008 GB/s | 84.17 GB (351%) | Out of Memory (OOM) | — |
| RTX 5090 32GB | 32 GB · 1792 GB/s | 84.17 GB (263%) | Out of Memory (OOM) | — |
| Apple M-Max 64GB | 64 GB · 546 GB/s | 82.79 GB (129%) | Out of Memory (OOM) | — |
| Mac Studio 128GB | 128 GB · 546 GB/s | 82.79 GB (65%) | Fits Comfortably | 3.7 tok/s |
| Mac Studio 192GB | 192 GB · 800 GB/s | 82.79 GB (43%) | Fits Comfortably | 5.5 tok/s |
| NVIDIA A100 80GB | 80 GB · 2039 GB/s | 84.17 GB (105%) | Out of Memory (OOM) | — |
| NVIDIA H100 80GB | 80 GB · 3350 GB/s | 84.17 GB (105%) | Out of Memory (OOM) | — |
Verified Transformer Architecture Parameters
- Checkpoint ID
- mistralai/Mistral-Large-Instruct-2411
- Ollama Tag
- mistral-large:123b
- License / Release
- Mistral Research · 2024-Q4
- Topology & Attention
- Dense Transformer
- Attention Mechanism
- Grouped-Query Attention (GQA)
- Total Params (B)
- 123B
- Active Params / Token (B)
- 123B
- Hidden Layers / Hidden Size
- 88 / 12288
- Query Heads / KV Heads
- 96Q / 8KV (d=128)
- Vocab / Max Position
- 32,768 / 131,072
Quantized formats store per-block scale factors and zero-points alongside packed integer weights. A 4-bit GGUF Q4_K_M checkpoint averages 4.85 effective bits per parameter (not 4.00), while Q8_0 averages 8.50 bpw. For Mixture-of-Experts models, all routed and shared experts remain resident in memory to prevent token-by-token host paging.
Standard Grouped-Query Attention allocates 2 × L × H_kv × d_head × S × B × bytes_kv across layers. DeepSeek Multi-head Latent Attention (MLA) compresses keys and values into L × (d_kv_lora + d_qk_rope) × S × B × bytes_kv, slashing long-context memory by over 90%. Hybrid sliding-window models cap local-attention layers at min(S, window_size) while scaling global layers linearly with S.
Quick-Start vLLM & Ollama Commands for Mistral Large 2411 (123B)
vllm serve mistralai/Mistral-Large-Instruct-2411 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 8 \
--gpu-memory-utilization 0.95 \
--kv-cache-dtype fp8 \
--enable-prefix-caching \
--port 8000# Save as Modelfile, then run: ollama create mistral-large-123b-custom -f Modelfile
FROM mistral-large:123b
PARAMETER num_ctx 32768
PARAMETER num_batch 512
PARAMETER flash_attn true
# Environment variables for ollama serve:
# OLLAMA_NUM_PARALLEL=8 OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serveCompare Sibling & Similar-Scale Open-Weight Models
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