Can DeepSeek Coder V2 236B run on H100 NVL 188GB?

BARELY — Tight on Memory

A73Great

Estimated from fit model

DeepSeek Coder V2 236B needs ~222.3 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~77 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 222.3 GB, 76.5 tok/s, Very compromised (needs ~22.2 GB host RAM)

222.3 GB required188.0 GB available

118% VRAM needed

34.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~22.2 GB host RAM)

Decode

76.5 tok/s

TTFT

2530 ms

Safe context

Memory

222.3 GB / 188.0 GB

Offload

20%

Memory breakdown

Weights144.0 GB

KV Cache58.6 GB

Runtime0.9 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 236B on H100 NVL 188GB
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: 76.5 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 20% 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 {ram} 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	S	Runs with offload	96.6 tok/s	1093 ms	7K
Coding	A	Very compromised	76.5 tok/s	2530 ms	7K
Agentic Coding	F	Too heavy	52.0 tok/s	5415 ms	7K
Reasoning	A	Very compromised	76.5 tok/s	2990 ms	7K
RAG	F	Too heavy	52.0 tok/s	6769 ms	7K

Quantization options

How DeepSeek Coder V2 236B (236B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	A83
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

Your hardware

More models your H100 NVL 188GB can run

Model	Params	Grade	Decode	Capabilities
DeepSeek V4 Flash	284B	S	136.1 tok/s

Frequently asked questions

Can H100 NVL 188GB run DeepSeek Coder V2 236B?

Yes, H100 NVL 188GB can run DeepSeek Coder V2 236B with a A grade (Very compromised). Expected decode speed: 76.5 tok/s.

How much VRAM does DeepSeek Coder V2 236B need?

DeepSeek Coder V2 236B (236B parameters) requires approximately 222.3 GB of memory with Q4_K_M quantization.

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

The recommended quantization for DeepSeek Coder V2 236B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek Coder V2 236B run at on H100 NVL 188GB?

On H100 NVL 188GB, DeepSeek Coder V2 236B achieves approximately 76.5 tokens per second decode speed with a time-to-first-token of 2530ms using Q4_K_M quantization.

Can H100 NVL 188GB run DeepSeek Coder V2 236B for coding?

For coding workloads, DeepSeek Coder V2 236B on H100 NVL 188GB receives a A grade with 76.5 tok/s and 7K context.

What context window can DeepSeek Coder V2 236B use on H100 NVL 188GB?

On H100 NVL 188GB, DeepSeek Coder V2 236B can safely use up to 7K tokens of context. 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 H100 NVL 188GB?

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 H100 NVL 188GB See all hardware for DeepSeek Coder V2 236B

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