MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)
C
This setup is broadly balanced for this model.76.8 tok/s Dekodierung
ca. $2,499 MSRP
Can granite 8b code instruct 4k run on NVIDIA V100 32GB?
YES — Runs Great
C49Usable
Estimated from fit model
granite 8b code instruct 4k needs ~10.2 GB VRAM. NVIDIA V100 32GB has 32.0 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) — 10.2 GB, 112.0 tok/s, Runs well
10.2 GB required32.0 GB available
32% VRAM used
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
388K
Memory
10.2 GB / 32.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on NVIDIA V100 32GB
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 | 388K |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 388K |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 388K |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 388K |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 388K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C43 |
Q3_K_S | 3 | 3.9 GB | Low | C43 |
NVFP4 | 4 | 4.5 GB | Medium | C43 |
Q4_K_M | 4 | 4.9 GB | Medium | C44 |
Q5_K_M | 5 | 5.8 GB | High | C44 |
Q6_K | 6 | 6.6 GB | High | C44 |
Q8_0 | 8 | 8.6 GB | Very High | C45 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C49 |
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 V100 32GB run granite 8b code instruct 4k?
Yes, NVIDIA V100 32GB 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 10.2 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 V100 32GB?
On NVIDIA V100 32GB, 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 V100 32GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on NVIDIA V100 32GB receives a C grade with 112.0 tok/s and 388K context.
What context window can granite 8b code instruct 4k use on NVIDIA V100 32GB?
On NVIDIA V100 32GB, granite 8b code instruct 4k can safely use up to 388K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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