Can DeepSeek Coder V2 236B run on NVIDIA B200 180GB?

YES — With NVFP4

A80Great

Estimated from fit model

DeepSeek Coder V2 236B needs ~209.7 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With NVFP4 quantization, expect ~95 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

DeepSeek Coder V2 236B at Q4_K_M needs 221.5 GB — too much for NVIDIA B200 180GB (180.0 GB). Runs at NVFP4 (209.7 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 221.5 GB, exceeds 180.0 GB available

221.5 GB required180.0 GB available

123% VRAM needed

41.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

76.2 tok/s

TTFT

2541 ms

Safe context

Memory

221.5 GB / 180.0 GB

Offload

20%

Memory breakdown

Weights144.0 GB

KV Cache58.6 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek Coder V2 236B on NVIDIA B200 180GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 76.2 tok/s decode · 2.5s TTFT (warm) · 191 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 18.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~9.1 GB host RAM)	96.3 tok/s	1097 ms	5K
Coding	F	Too heavy	76.2 tok/s	2541 ms	5K
Agentic Coding	F	Too heavy	51.7 tok/s	5444 ms	5K
Reasoning	F	Too heavy	76.2 tok/s	3003 ms	5K
RAG	F	Too heavy	51.7 tok/s	6805 ms	5K

Inference speed

DeepSeek Coder V2 236B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek Coder V2 236B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~12 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	11.7	Too big
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	6.1	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	5.7	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	4.5	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.5	Too big
RTX 5090 32GB	32 GB	Q4_K_M	3.4	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	3.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	3.1	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.9	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.7	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.3	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.2	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek Coder V2 236B (236B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	A84
Q3_K_S	3	115.6 GB	Low	A84
NVFP4	4	132.2 GB	Medium	A84
Q4_K_MBest for your GPU	4	144.0 GB	Medium	A84
Q5_K_M	5	169.9 GB	High	F0
Q6_K	6	193.5 GB	High	F0
Q8_0	8	252.5 GB	Very High	F0
F16	16	483.8 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek Coder V2 236B on your machine.

Run

lms load DeepSeek-Coder-V2-Instruct && lms server start

Upgrade-Optionen

Hardware, die DeepSeek Coder V2 236B gut ausführt

AMD Instinct MI350X 288GBGünstige Wahl

288 GB VRAM (+108)

Makes the model fit on the accelerator instead of staying completely out of reach.109.3 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $8,000 MSRP

B100 192GBBestes Preis-Leistungs-Verhältnis

192 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.84 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

ca. $35,000 MSRP

NVIDIA GB200 192GBNVIDIA-Upgrade

192 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.84 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

ca. $60,000 MSRP

Frequently asked questions

Can NVIDIA B200 180GB run DeepSeek Coder V2 236B?

Yes, NVIDIA B200 180GB can run DeepSeek Coder V2 236B at NVFP4 quantization (Very compromised (needs ~18.7 GB host RAM)). The recommended Q4_K_M requires 221.5 GB which exceeds available memory, but at NVFP4 it needs only 209.7 GB. Expected decode speed: 95.4 tok/s.

How much VRAM does DeepSeek Coder V2 236B need?

DeepSeek Coder V2 236B (236B parameters) requires approximately 221.5 GB at Q4_K_M quantization. On NVIDIA B200 180GB, it fits at NVFP4 using 209.7 GB.

What is the best quantization for DeepSeek Coder V2 236B?

The recommended quantization is Q4_K_M, but on NVIDIA B200 180GB the best fitting quantization is NVFP4, which uses 209.7 GB.

What speed will DeepSeek Coder V2 236B run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, DeepSeek Coder V2 236B achieves approximately 95.4 tokens per second decode speed with a time-to-first-token of 2030ms using NVFP4 quantization.

Can NVIDIA B200 180GB run DeepSeek Coder V2 236B for coding?

For coding workloads, DeepSeek Coder V2 236B on NVIDIA B200 180GB receives a F grade with 76.2 tok/s and 5K context.

What context window can DeepSeek Coder V2 236B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, DeepSeek Coder V2 236B can safely use up to 8K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek Coder V2 236B feels slow on NVIDIA B200 180GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for NVIDIA B200 180GB See all hardware for DeepSeek Coder V2 236B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/deepseek-coder-v2-236b-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: