Can Granite Code 8B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

A70Great

Estimated from fit model

Granite Code 8B needs ~22.1 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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) — 22.1 GB, 112.0 tok/s, Runs well

22.1 GB required141.0 GB available

16% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

Memory

22.1 GB / 141.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsGranite Code 8B on NVIDIA H200 PCIe 141GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	112.0 tok/s	943 ms	8K
Coding	A	Runs well	112.0 tok/s	1729 ms	8K
Agentic Coding	A	Runs well	112.0 tok/s	2514 ms	8K
Reasoning	A	Runs well	112.0 tok/s	2043 ms	8K
RAG	A	Runs well	112.0 tok/s	3143 ms	8K

Inference speed

Granite Code 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite Code 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	102.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	96.9	Fits
RTX 4070 12GB	12 GB	Q4_K_M	83.3	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	82.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	82.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
RTX 3060 12GB	12 GB	Q4_K_M	52.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	26.6	Heavy offload

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 Granite Code 8B (8B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B63
Q3_K_S	3	3.9 GB	Low	B63
NVFP4	4	4.5 GB	Medium	B63
Q4_K_M	4	4.9 GB	Medium	B63
Q5_K_M	5	5.8 GB	High	B63
Q6_K	6	6.6 GB	High	B63
Q8_0	8	8.6 GB	Very High	B63
F16Best for your GPU	16	16.4 GB	Maximum	B63

Get started

Copy-paste commands to run Granite Code 8B on your machine.

Run

ollama run granite-code:8b

Your hardware

More models your NVIDIA H200 PCIe 141GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	58.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	609.7 tok/s
Qwen 3.5 27B	27B	S	264.4 tok/s
Qwen 3.6 27B	27B	S	265.2 tok/s
Qwen 3.5 122B A10B	122B	S	162.1 tok/s

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Granite Code 8B?

Yes, NVIDIA H200 PCIe 141GB can run Granite Code 8B with a A grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 22.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

The recommended quantization for Granite Code 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 8B run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Granite Code 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run Granite Code 8B for coding?

For coding workloads, Granite Code 8B on NVIDIA H200 PCIe 141GB receives a A grade with 112.0 tok/s and 8K context.

What context window can Granite Code 8B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Granite Code 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GB See all hardware for Granite Code 8B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

112.0 tok/s

943 ms

Coding

Runs well

112.0 tok/s

1729 ms

Agentic Coding

Runs well

112.0 tok/s

2514 ms

Reasoning

Runs well

112.0 tok/s

2043 ms

RAG

Runs well

112.0 tok/s

3143 ms

Granite Code 8B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

112.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

112.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

112.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

112.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

112.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

112.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

102.2

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

96.9

Fits

RTX 4070 12GB

12 GB

Q4_K_M

83.3

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

82.6

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

82.6

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

52.9

Fits

RTX 3060 12GB

12 GB

Q4_K_M

52.3

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

48.5

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

42.6

Fits

RTX 4060 8GB

8 GB

Q4_K_M

26.6

Heavy offload

Quantization options

How Granite Code 8B (8B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B63
Q3_K_S	3	3.9 GB	Low	B63
NVFP4	4	4.5 GB	Medium	B63
Q4_K_M	4	4.9 GB	Medium	B63
Q5_K_M	5	5.8 GB	High	B63
Q6_K	6	6.6 GB	High	B63
Q8_0	8	8.6 GB	Very High	B63
F16Best for your GPU	16	16.4 GB	Maximum	B63

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

58.4 tok/s

Qwen3-Coder 30B A3B Instruct

30.5B

609.7 tok/s

Qwen 3.5 27B

27B

264.4 tok/s

Qwen 3.6 27B

27B

265.2 tok/s

Qwen 3.5 122B A10B

122B

162.1 tok/s

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Granite Code 8B?

Yes, NVIDIA H200 PCIe 141GB can run Granite Code 8B with a A grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 22.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

The recommended quantization for Granite Code 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 8B run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Granite Code 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run Granite Code 8B for coding?

For coding workloads, Granite Code 8B on NVIDIA H200 PCIe 141GB receives a A grade with 112.0 tok/s and 8K context.

What context window can Granite Code 8B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Granite Code 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.