FitMyLLM
Mistral AI/Dense

Mistral AIMistral Medium 3.5

Mistral Medium 3.5 is our first flagship merged model. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning,

chatcodingreasoningmathvisionagentictool_use
128B
Parameters
256K
Context length
18
Benchmarks
17
Quantizations
0
Architecture
Dense
Released
2026-04-30
Layers
88
KV Heads
8
Head Dim
128
Family
mistral

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ2_XXS2.38
42.7 GB
38.6 + 4.1 KV
low
IQ2_M2.93
51.5 GB
47.4 + 4.1 KV
low
Q2_K3.16
55.2 GB
51.0 + 4.1 KV
low
IQ3_XXS3.25
56.6 GB
52.5 + 4.1 KV
low
IQ3_XS3.5
60.6 GB
56.5 + 4.1 KV
low
Q3_K_S3.64
62.9 GB
58.7 + 4.1 KV
low
IQ3_M3.76
64.8 GB
60.6 + 4.1 KV
low
Q3_K_M4
68.6 GB
64.5 + 4.1 KV
low
Q3_K_L4.3
73.4 GB
69.3 + 4.1 KV
moderate
IQ4_XS4.46
76.0 GB
71.8 + 4.1 KV
moderate
Q4_K_S4.67
79.3 GB
75.2 + 4.1 KV
moderate
Q4_K_M4.89
82.9 GB
78.7 + 4.1 KV
good
Q5_K_S5.57
93.7 GB
89.6 + 4.1 KV
good
Q5_K_M5.7
95.8 GB
91.7 + 4.1 KV
good
Q6_K6.56
109.6 GB
105.4 + 4.1 KV
excellent
Q8_08.5
140.6 GB
136.5 + 4.1 KV
lossless
FP1616
260.6 GB
256.5 + 4.1 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
▸ READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN MISTRAL MEDIUM 3.5 NOW

Rent on Vast.ai →Or RunPod →

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (18)

τ²-Bench94.2
IFEval84.0
GPQA Diamond74.8
IFBench68.8
AA Long Context61.0
BBH52.7
MMLU-PRO50.7
MATH49.5
LiveCodeBench40.0
SciCode39.6
AA Intelligence39.2
AA Coding35.4
Terminal-Bench33.3
AIME30.3
MATH-50030.3
GPQA24.9
MUSR17.2
HLE12.8

Run this model

▸Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run mistral-medium-3.5:128b-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines
HuggingFaceOllama LibraryGGUF DownloadsBuild Hardware

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA H100 SXM5 80GB
80 GB VRAM • 3350 GB/s
NVIDIA
$25000
NVIDIA H100 PCIe 80GB
80 GB VRAM • 2000 GB/s
NVIDIA
$25000
NVIDIA A100 SXM 80GB
80 GB VRAM • 2039 GB/s
NVIDIA
$10000
NVIDIA A100 PCIe 80GB
80 GB VRAM • 1935 GB/s
NVIDIA
$10000
NVIDIA A100 SXM4 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
$15000
NVIDIA A100 PCIe 80 GB
80 GB VRAM • 1940 GB/s
NVIDIA
$10000
NVIDIA A100X
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H100 PCIe 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
$25000
NVIDIA H100 SXM5 80 GB
80 GB VRAM • 3360 GB/s
NVIDIA
$25000
NVIDIA H100 CNX
80 GB VRAM • 2040 GB/s
NVIDIA
$25000
NVIDIA A800 PCIe 80 GB
80 GB VRAM • 1940 GB/s
NVIDIA
NVIDIA A800 SXM4 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H800 PCIe 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H800 SXM5
80 GB VRAM • 3360 GB/s
NVIDIA
NVIDIA RTX 6000D
84 GB VRAM • 1570 GB/s
NVIDIA
$7500
NVIDIA B200
90 GB VRAM • 4100 GB/s
NVIDIA
$30000
NVIDIA H100 NVL 94 GB
94 GB VRAM • 3940 GB/s
NVIDIA
$30000
NVIDIA H100 SXM5 94 GB
94 GB VRAM • 3360 GB/s
NVIDIA
$25000
RTX Pro 6000
96 GB VRAM • 1792 GB/s
NVIDIA
$8565
NVIDIA H100 PCIe 96 GB
96 GB VRAM • 3360 GB/s
NVIDIA
$25000
NVIDIA H100 SXM5 96 GB
96 GB VRAM • 3360 GB/s
NVIDIA
$25000
Intel Data Center GPU Max 1350
96 GB VRAM • 2460 GB/s
INTEL
NVIDIA RTX PRO 6000 Blackwell Server
96 GB VRAM • 1790 GB/s
NVIDIA
$9999
NVIDIA RTX PRO 6000 Blackwell
96 GB VRAM • 1790 GB/s
NVIDIA
$9999
AMD Instinct MI300A
120 GB VRAM • 5300 GB/s
AMD
$12000
Apple M4 Max (128GB)
128 GB VRAM • 546 GB/s
APPLE
$3999
AMD Instinct MI250X
128 GB VRAM • 3277 GB/s
AMD
$10000
Apple M1 Ultra (128GB)
128 GB VRAM • 800 GB/s
APPLE
$4999
Apple M2 Ultra (128GB)
128 GB VRAM • 800 GB/s
APPLE
$3999
AMD Radeon Instinct MI250
128 GB VRAM • 3280 GB/s
AMD
$12000

Find the best GPU for Mistral Medium 3.5

Build Hardware for Mistral Medium 3.5
▸ SPEC SHEET

Mistral Medium 3.5 — 128B Dense.

▸ SPECIFICATIONS
PARAMETERS
128B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat, coding, reasoning, math, vision, agentic, tool_use
RELEASE DATE
2026-04-30
PROVIDER
Mistral AI
FAMILY
mistral
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ2_XXS2.3838.6 GB65%
IQ2_M2.9347.4 GB75%
Q2_K3.1651.0 GB78%
IQ3_XXS3.2552.5 GB82%
IQ3_XS3.556.5 GB84%
Q3_K_S3.6458.7 GB85%
IQ3_M3.7660.6 GB86%
Q3_K_M464.5 GB88%
Q3_K_L4.369.3 GB90%
IQ4_XS4.4671.8 GB92%
Q4_K_S4.6775.2 GB93%
Q4_K_M4.8978.7 GB94%
Q5_K_S5.5789.6 GB96%
Q5_K_M5.791.7 GB96%
Q6_K6.56105.4 GB97%
Q8_08.5136.5 GB100%
FP1616256.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO50.7
MATH49.5
IFEval84.0
BBH52.7
GPQA24.9
MUSR17.2
GPQA Diamond74.8
LiveCodeBench40.0
AIME30.3
HLE12.8
AA Intelligence39.2
AA Coding35.4
aa_ifbench68.8
aa_terminal_bench33.3
aa_tau294.2
aa_scicode39.6
aa_lcr61.0
MATH-50030.3
§ 02RUN COMMAND

Run Mistral Medium 3.5 locally with Ollama — needs 78.7 GB VRAM at Q4_K_M:

$ollama run mistral-medium-3.5:128b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M
NVIDIA H100 SXM5 80GB
80 GB · 3350 GB/s
NVIDIA H100 PCIe 80GB
80 GB · 2000 GB/s
NVIDIA A100 SXM 80GB
80 GB · 2039 GB/s
NVIDIA A100 PCIe 80GB
80 GB · 1935 GB/s
NVIDIA A100 SXM4 80 GB
80 GB · 2040 GB/s
NVIDIA A100 PCIe 80 GB
80 GB · 1940 GB/s
NVIDIA A100X
80 GB · 2040 GB/s
NVIDIA H100 PCIe 80 GB
80 GB · 2040 GB/s
NVIDIA H100 SXM5 80 GB
80 GB · 3360 GB/s
NVIDIA H100 CNX
80 GB · 2040 GB/s
NVIDIA A800 PCIe 80 GB
80 GB · 1940 GB/s
NVIDIA A800 SXM4 80 GB
80 GB · 2040 GB/s
NVIDIA H800 PCIe 80 GB
80 GB · 2040 GB/s
NVIDIA H800 SXM5
80 GB · 3360 GB/s
NVIDIA RTX 6000D
84 GB · 1570 GB/s