Q6_K63GB VRAM minimum

Deepseek AI Deepseek Coder V2 Instruct 0724 Q6_K VRAM Requirements

This page answers Deepseek AI Deepseek Coder V2 Instruct 0724 q6_k quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.

Short answer

Direct requirement summary for Deepseek AI Deepseek Coder V2 Instruct 0724 Q6_K

Short answer: Deepseek AI Deepseek Coder V2 Instruct 0724 typically needs around 63GB VRAM at Q6_K, and 76GB is safer for smoother usage.

Minimum VRAM

63GB

Recommended VRAM

76GB

Target quantization

Q6_K

Requirement Snapshot

Current quantization-specific requirement breakdown

Selected quantizationQ6_K

Minimum VRAM63GB

Q4 baseline36GB

Q8 baseline72GB

FP16 baseline144GB

Methodology

No hand-wavy numbers

Estimated between Q4 and Q8 using a weighted interpolation toward Q8 memory footprint.

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 Deepseek AI Deepseek Coder V2 Instruct 0724? →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 Deepseek AI Deepseek Coder V2 Instruct 0724? →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 Deepseek AI Deepseek Coder V2 Instruct 0724? →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 Deepseek AI Deepseek Coder V2 Instruct 0724

Q4 requirements Q4_K_M requirements Q5_K_M requirements Q8 requirements FP16 requirements

Best GPUs for Deepseek AI Deepseek Coder V2 Instruct 0724 (Q6_K)

GPU	VRAM	Quantization	Speed	Compatibility	Buy
AMD Instinct MI300X	192GB	Q8	134 tok/s	View full compatibility	Buy options
NVIDIA H200 SXM 141GB	141GB	Q8	121 tok/s	View full compatibility	Buy options
NVIDIA H100 SXM5 80GB	80GB	Q8	87 tok/s	View full compatibility	Buy options
AMD Instinct MI250X	128GB	Q8	84 tok/s	View full compatibility	Buy options
NVIDIA H100 PCIe 80GB	80GB	Q8	55 tok/s	View full compatibility	Buy options
RTX 5090	32GB	Q8	52 tok/s	View full compatibility	Buy options
NVIDIA A100 80GB SXM4	80GB	Q8	51 tok/s	View full compatibility	Buy options
AMD Instinct MI210	64GB	Q8	42 tok/s	View full compatibility	Buy options
NVIDIA A100 40GB PCIe	40GB	Q8	40 tok/s	View full compatibility	Buy options
RTX 4090	24GB	Q8	32 tok/s	View full compatibility	Buy options
NVIDIA RTX 6000 Ada	48GB	Q8	31 tok/s	View full compatibility	Buy options
NVIDIA L40	48GB	Q8	29 tok/s	View full compatibility	Buy options

Back to Deepseek AI Deepseek Coder V2 Instruct 0724 model page Full hardware requirements Best GPU guides Prebuilt systems Local AI build guides

VRAM requirements FAQ

How much VRAM does Deepseek AI Deepseek Coder V2 Instruct 0724 need at Q6_K?

Deepseek AI Deepseek Coder V2 Instruct 0724 at Q6_K is estimated to require about 63GB VRAM minimum, with 76GB recommended for smoother operation.

Which GPUs can run Deepseek AI Deepseek Coder V2 Instruct 0724 Q6_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 Q6_K or a different quantization for Deepseek AI Deepseek Coder V2 Instruct 0724?

Q6_K is a balance point between memory usage and quality. If your GPU is below 63GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.

Q6_K63GB VRAM minimum

Deepseek AI Deepseek Coder V2 Instruct 0724 Q6_K VRAM Requirements

This page answers Deepseek AI Deepseek Coder V2 Instruct 0724 q6_k quantization queries with explicit calculations from our model requirement dataset and compatibility speed table.

Short answer

Direct requirement summary for Deepseek AI Deepseek Coder V2 Instruct 0724 Q6_K

Short answer: Deepseek AI Deepseek Coder V2 Instruct 0724 typically needs around 63GB VRAM at Q6_K, and 76GB is safer for smoother usage.

Minimum VRAM

63GB

Recommended VRAM

76GB

Target quantization

Q6_K

Requirement Snapshot

Current quantization-specific requirement breakdown

Selected quantizationQ6_K

Minimum VRAM63GB

Q4 baseline36GB

Q8 baseline72GB

FP16 baseline144GB

Methodology

No hand-wavy numbers

Estimated between Q4 and Q8 using a weighted interpolation toward Q8 memory footprint.

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 Deepseek AI Deepseek Coder V2 Instruct 0724? →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 Deepseek AI Deepseek Coder V2 Instruct 0724? →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 Deepseek AI Deepseek Coder V2 Instruct 0724? →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 Deepseek AI Deepseek Coder V2 Instruct 0724

Q4 requirements Q4_K_M requirements Q5_K_M requirements Q8 requirements FP16 requirements

Best GPUs for Deepseek AI Deepseek Coder V2 Instruct 0724 (Q6_K)

GPU	VRAM	Quantization	Speed	Compatibility	Buy
AMD Instinct MI300X	192GB	Q8	134 tok/s	View full compatibility	Buy options
NVIDIA H200 SXM 141GB	141GB	Q8	121 tok/s	View full compatibility	Buy options
NVIDIA H100 SXM5 80GB	80GB	Q8	87 tok/s	View full compatibility	Buy options
AMD Instinct MI250X	128GB	Q8	84 tok/s	View full compatibility	Buy options
NVIDIA H100 PCIe 80GB	80GB	Q8	55 tok/s	View full compatibility	Buy options
RTX 5090	32GB	Q8	52 tok/s	View full compatibility	Buy options
NVIDIA A100 80GB SXM4	80GB	Q8	51 tok/s	View full compatibility	Buy options
AMD Instinct MI210	64GB	Q8	42 tok/s	View full compatibility	Buy options
NVIDIA A100 40GB PCIe	40GB	Q8	40 tok/s	View full compatibility	Buy options
RTX 4090	24GB	Q8	32 tok/s	View full compatibility	Buy options
NVIDIA RTX 6000 Ada	48GB	Q8	31 tok/s	View full compatibility	Buy options
NVIDIA L40	48GB	Q8	29 tok/s	View full compatibility	Buy options

Back to Deepseek AI Deepseek Coder V2 Instruct 0724 model page Full hardware requirements Best GPU guides Prebuilt systems Local AI build guides

VRAM requirements FAQ

How much VRAM does Deepseek AI Deepseek Coder V2 Instruct 0724 need at Q6_K?

Deepseek AI Deepseek Coder V2 Instruct 0724 at Q6_K is estimated to require about 63GB VRAM minimum, with 76GB recommended for smoother operation.

Which GPUs can run Deepseek AI Deepseek Coder V2 Instruct 0724 Q6_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 Q6_K or a different quantization for Deepseek AI Deepseek Coder V2 Instruct 0724?

Q6_K is a balance point between memory usage and quality. If your GPU is below 63GB, consider lower-bit quantization; if you have extra VRAM, compare Q8/FP16 options for quality-sensitive workloads.