Q2_K186GB VRAM minimum

Mistralai Mistral Large 3 675B Instruct 2512 Q2_K VRAM Requirements

This page answers Mistralai Mistral Large 3 675B Instruct 2512 q2_k quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.

Short answer

Direct requirement summary for Mistralai Mistral Large 3 675B Instruct 2512 Q2_K

Short answer: Mistralai Mistral Large 3 675B Instruct 2512 typically needs around 186GB VRAM at Q2_K, and 224GB is safer for smoother usage.

Minimum VRAM

186GB

Recommended VRAM

224GB

Target quantization

Q2_K

Requirement Snapshot

Current quantization-specific requirement breakdown

Selected quantizationQ2_K

Minimum VRAM186GB

Q4 baseline338GB

Q8 baseline675GB

FP16 baseline1350GB

Methodology

No hand-wavy numbers

Estimated from Q4 using a 45% memory reduction assumption for Q2_K.

Throughput data below uses available compatibility measurements/estimates and is sorted by tokens per second for this model.

Need general guidance? Review full methodology.

Next steps for this requirement

AMD Instinct MI300X

Check full compatibility details and speed context for this model.

Can AMD Instinct MI300X run Mistralai Mistral Large 3 675B Instruct 2512? →Buy options for AMD Instinct MI300X →

NVIDIA H200 SXM 141GB

Check full compatibility details and speed context for this model.

Can NVIDIA H200 SXM 141GB run Mistralai Mistral Large 3 675B Instruct 2512? →Buy options for NVIDIA H200 SXM 141GB →

NVIDIA H100 SXM5 80GB

Check full compatibility details and speed context for this model.

Can NVIDIA H100 SXM5 80GB run Mistralai Mistral Large 3 675B Instruct 2512? →Buy options for NVIDIA H100 SXM5 80GB →

Need GPU recommendations?

Compare curated best GPU guides by budget and workload.

Browse best GPU guides →

Need a complete build?

Use proven local AI build recipes if you are planning a fresh hardware setup.

Browse local AI builds →

Prefer prebuilt systems?

Compare ready-to-buy systems if you want faster deployment.

Compare prebuilt systems →

Compare other quantization tiers for Mistralai Mistral Large 3 675B Instruct 2512

Q4 requirements Q4_K_M requirements Q5_K_M requirements Q8 requirements FP16 requirements

Best GPUs for Mistralai Mistral Large 3 675B Instruct 2512 (Q2_K)

GPU	VRAM	Quantization	Speed	Compatibility	Buy
AMD Instinct MI300X	192GB	Q4	92 tok/s	View full compatibility	Buy options
NVIDIA H200 SXM 141GB	141GB	Q4	83 tok/s	View full compatibility	Buy options
NVIDIA H100 SXM5 80GB	80GB	Q4	59 tok/s	View full compatibility	Buy options
AMD Instinct MI250X	128GB	Q4	57 tok/s	View full compatibility	Buy options
NVIDIA H100 PCIe 80GB	80GB	Q4	38 tok/s	View full compatibility	Buy options
RTX 5090	32GB	Q4	36 tok/s	View full compatibility	Buy options
NVIDIA A100 80GB SXM4	80GB	Q4	35 tok/s	View full compatibility	Buy options
AMD Instinct MI210	64GB	Q4	29 tok/s	View full compatibility	Buy options
NVIDIA A100 40GB PCIe	40GB	Q4	27 tok/s	View full compatibility	Buy options
RTX 4090	24GB	Q4	22 tok/s	View full compatibility	Buy options
NVIDIA RTX 6000 Ada	48GB	Q4	21 tok/s	View full compatibility	Buy options
NVIDIA L40	48GB	Q4	20 tok/s	View full compatibility	Buy options

Back to Mistralai Mistral Large 3 675B Instruct 2512 model page Full hardware requirements Best GPU guides Prebuilt systems Local AI build guides

VRAM requirements FAQ

How much VRAM does Mistralai Mistral Large 3 675B Instruct 2512 need at Q2_K?

Mistralai Mistral Large 3 675B Instruct 2512 at Q2_K is estimated to require about 186GB VRAM minimum, with 224GB recommended for smoother operation.

Which GPUs can run Mistralai Mistral Large 3 675B Instruct 2512 Q2_K?

Start with AMD Instinct MI300X, NVIDIA H200 SXM 141GB, NVIDIA H100 SXM5 80GB and review each compatibility page for full speed and fit details.

Should I use Q2_K or a different quantization for Mistralai Mistral Large 3 675B Instruct 2512?

Q2_K is a balance point between memory usage and quality. If your GPU is below 186GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.

Q2_K186GB VRAM minimum

Mistralai Mistral Large 3 675B Instruct 2512 Q2_K VRAM Requirements

This page answers Mistralai Mistral Large 3 675B Instruct 2512 q2_k quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.

Short answer

Direct requirement summary for Mistralai Mistral Large 3 675B Instruct 2512 Q2_K

Short answer: Mistralai Mistral Large 3 675B Instruct 2512 typically needs around 186GB VRAM at Q2_K, and 224GB is safer for smoother usage.

Minimum VRAM

186GB

Recommended VRAM

224GB

Target quantization

Q2_K

Requirement Snapshot

Current quantization-specific requirement breakdown

Selected quantizationQ2_K

Minimum VRAM186GB

Q4 baseline338GB

Q8 baseline675GB

FP16 baseline1350GB

Methodology

No hand-wavy numbers

Estimated from Q4 using a 45% memory reduction assumption for Q2_K.

Throughput data below uses available compatibility measurements/estimates and is sorted by tokens per second for this model.

Need general guidance? Review full methodology.

Next steps for this requirement

AMD Instinct MI300X

Check full compatibility details and speed context for this model.

Can AMD Instinct MI300X run Mistralai Mistral Large 3 675B Instruct 2512? →Buy options for AMD Instinct MI300X →

NVIDIA H200 SXM 141GB

Check full compatibility details and speed context for this model.

Can NVIDIA H200 SXM 141GB run Mistralai Mistral Large 3 675B Instruct 2512? →Buy options for NVIDIA H200 SXM 141GB →

NVIDIA H100 SXM5 80GB

Check full compatibility details and speed context for this model.

Can NVIDIA H100 SXM5 80GB run Mistralai Mistral Large 3 675B Instruct 2512? →Buy options for NVIDIA H100 SXM5 80GB →

Need GPU recommendations?

Compare curated best GPU guides by budget and workload.

Browse best GPU guides →

Need a complete build?

Use proven local AI build recipes if you are planning a fresh hardware setup.

Browse local AI builds →

Prefer prebuilt systems?

Compare ready-to-buy systems if you want faster deployment.

Compare prebuilt systems →

Compare other quantization tiers for Mistralai Mistral Large 3 675B Instruct 2512

Q4 requirements Q4_K_M requirements Q5_K_M requirements Q8 requirements FP16 requirements

Best GPUs for Mistralai Mistral Large 3 675B Instruct 2512 (Q2_K)

GPU	VRAM	Quantization	Speed	Compatibility	Buy
AMD Instinct MI300X	192GB	Q4	92 tok/s	View full compatibility	Buy options
NVIDIA H200 SXM 141GB	141GB	Q4	83 tok/s	View full compatibility	Buy options
NVIDIA H100 SXM5 80GB	80GB	Q4	59 tok/s	View full compatibility	Buy options
AMD Instinct MI250X	128GB	Q4	57 tok/s	View full compatibility	Buy options
NVIDIA H100 PCIe 80GB	80GB	Q4	38 tok/s	View full compatibility	Buy options
RTX 5090	32GB	Q4	36 tok/s	View full compatibility	Buy options
NVIDIA A100 80GB SXM4	80GB	Q4	35 tok/s	View full compatibility	Buy options
AMD Instinct MI210	64GB	Q4	29 tok/s	View full compatibility	Buy options
NVIDIA A100 40GB PCIe	40GB	Q4	27 tok/s	View full compatibility	Buy options
RTX 4090	24GB	Q4	22 tok/s	View full compatibility	Buy options
NVIDIA RTX 6000 Ada	48GB	Q4	21 tok/s	View full compatibility	Buy options
NVIDIA L40	48GB	Q4	20 tok/s	View full compatibility	Buy options

Back to Mistralai Mistral Large 3 675B Instruct 2512 model page Full hardware requirements Best GPU guides Prebuilt systems Local AI build guides

VRAM requirements FAQ

How much VRAM does Mistralai Mistral Large 3 675B Instruct 2512 need at Q2_K?

Mistralai Mistral Large 3 675B Instruct 2512 at Q2_K is estimated to require about 186GB VRAM minimum, with 224GB recommended for smoother operation.

Which GPUs can run Mistralai Mistral Large 3 675B Instruct 2512 Q2_K?

Start with AMD Instinct MI300X, NVIDIA H200 SXM 141GB, NVIDIA H100 SXM5 80GB and review each compatibility page for full speed and fit details.

Should I use Q2_K or a different quantization for Mistralai Mistral Large 3 675B Instruct 2512?

Q2_K is a balance point between memory usage and quality. If your GPU is below 186GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.