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URL: https://willitrunai.com/can-run/granite-code-34b-on-h800-80gb


Can Granite Code 34B run on NVIDIA H800 80GB?

YES — Runs Great

A78Great
Estimated from fit model

Granite Code 34B needs ~33.6 GB VRAM. NVIDIA H800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~117 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) — 33.6 GB, 126.9 tok/s, Runs well
33.6 GB required80.0 GB available
42% VRAM used

Fit status

Runs well

Decode

126.9 tok/s

TTFT

1525 ms

Safe context

8K

Memory

33.6 GB / 80.0 GB

Memory breakdown

Weights20.7 GB
KV Cache3.7 GB
Runtime1.2 GB
Headroom8.0 GB

See how fast it feels

See how fast it feelsGranite Code 34B on NVIDIA H800 80GB
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: 126.9 tok/s decode · 1.5s TTFT (warm) · 317 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well117.2 tok/s901 ms8K
CodingARuns well117.2 tok/s1652 ms8K
Agentic CodingARuns well117.2 tok/s2403 ms8K
ReasoningARuns well117.2 tok/s1953 ms8K
RAGARuns well117.2 tok/s3004 ms8K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowB68
Q3_K_S
3
16.7 GB
LowB68
NVFP4
4

Get started

Copy-paste commands to run Granite Code 34B on your machine.

Run

ollama run granite-code:34b

Your hardware

More models your NVIDIA H800 80GB can run

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct
123BA24.9 tok/s
👁 Alibaba
Qwen 3.5 122B A10B
122BS

Frequently asked questions

See all results for NVIDIA H800 80GBSee all hardware for Granite Code 34B
19.0 GB
Medium
B69
Q4_K_M
4
20.7 GB
MediumB69
Q5_K_M
5
24.5 GB
HighB70
Q6_K
6
27.9 GB
HighA70
Q8_0Best for your GPU
8
36.4 GB
Very HighA72
F16
16
69.7 GB
MaximumF0
73.9 tok/s
👁 Alibaba
Qwen 3.6 35B A3B
35BS308.8 tok/s
👁 Alibaba
Qwen 3.5 35B A3B
35BS335.8 tok/s
👁 Mistral
Mistral Small 4 119B
119BA78.4 tok/s