Can DeepSeek Coder V2 236B run on NVIDIA GB200 192GB?

Q: What should I upgrade first if DeepSeek Coder V2 236B feels slow on NVIDIA GB200 192GB?

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.

BARELY — Tight on Memory

A79Great

Estimated from fit model

DeepSeek Coder V2 236B needs ~222.7 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~84 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) — 222.7 GB, 84.0 tok/s, Very compromised (needs ~19.8 GB host RAM)

222.7 GB required192.0 GB available

116% VRAM needed

30.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~19.8 GB host RAM)

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

Memory

222.7 GB / 192.0 GB

Offload

10%

Memory breakdown

Weights144.0 GB

KV Cache58.6 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 236B on NVIDIA GB200 192GB
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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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 19.8 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 (needs ~1 GB host RAM)	106.0 tok/s	996 ms	8K
Coding	A	Very compromised (needs ~19.8 GB host RAM)	84.0 tok/s	2305 ms	8K
Agentic Coding	F	Too heavy	57.1 tok/s	4929 ms	8K
Reasoning	A	Very compromised (needs ~19.8 GB host RAM)	84.0 tok/s	2724 ms	8K
RAG	F	Too heavy	57.1 tok/s	6161 ms

Quantization options

How DeepSeek Coder V2 236B (236B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 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

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 NVIDIA GB200 192GB can run

Model	Params	Grade	Decode	Capabilities
DeepSeek V4 Flash	284B	S	144.8 tok/s

Frequently asked questions

Can NVIDIA GB200 192GB run DeepSeek Coder V2 236B?

Yes, NVIDIA GB200 192GB can run DeepSeek Coder V2 236B with a A grade (Very compromised (needs ~19.8 GB host RAM)). Expected decode speed: 84.0 tok/s.

How much VRAM does DeepSeek Coder V2 236B need?

DeepSeek Coder V2 236B (236B parameters) requires approximately 222.7 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 NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, DeepSeek Coder V2 236B achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.

Can NVIDIA GB200 192GB run DeepSeek Coder V2 236B for coding?

For coding workloads, DeepSeek Coder V2 236B on NVIDIA GB200 192GB receives a A grade with 84.0 tok/s and 8K context.

What context window can DeepSeek Coder V2 236B use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, DeepSeek Coder V2 236B can safely use up to 8K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA GB200 192GB See all hardware for DeepSeek Coder V2 236B

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