Mac Studio M2 Ultra 128GBGünstige Wahl
128 GB Unified (+48)
C
This setup is broadly balanced for this model.95.1 tok/s Dekodierung
ca. $3,999 MSRP
Can granite 8b code instruct 4k run on NVIDIA H800 80GB?
YES — Runs Great
C46Usable
Estimated from fit model
granite 8b code instruct 4k needs ~15.0 GB VRAM. NVIDIA H800 80GB has 80.0 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.
Select quantization to explore
Q4_K_M (Medium quality) — 15.0 GB, 112.0 tok/s, Runs well
15.0 GB required80.0 GB available
19% VRAM used
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
1.1M
Memory
15.0 GB / 80.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k 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: 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 | C | Runs well | 112.0 tok/s | 943 ms | 1.1M |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 1.1M |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 1.1M |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 1.1M |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 1.1M |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | D40 |
Q3_K_S | 3 | 3.9 GB | Low | D40 |
NVFP4 | 4 | 4.5 GB | Medium | D40 |
Q4_K_M | 4 | 4.9 GB | Medium | D40 |
Q5_K_M | 5 | 5.8 GB | High | D40 |
Q6_K | 6 | 6.6 GB | High | D40 |
Q8_0 | 8 | 8.6 GB | Very High | D40 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C41 |
Get started
Copy-paste commands to run granite 8b code instruct 4k on your machine.
Run
lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server startUpgrade-Optionen
Hardware, die granite 8b code instruct 4k gut ausführt
Frequently asked questions
Can NVIDIA H800 80GB run granite 8b code instruct 4k?
Yes, NVIDIA H800 80GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 112.0 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.
What is the best quantization for granite 8b code instruct 4k?
The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.
What speed will granite 8b code instruct 4k run at on NVIDIA H800 80GB?
On NVIDIA H800 80GB, granite 8b code instruct 4k achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
Can NVIDIA H800 80GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on NVIDIA H800 80GB receives a C grade with 112.0 tok/s and 1.1M context.
What context window can granite 8b code instruct 4k use on NVIDIA H800 80GB?
On NVIDIA H800 80GB, granite 8b code instruct 4k can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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