Mac Studio M2 Ultra 128GBコスパ重視
128 GB Unified (+48)
C
This setup is broadly balanced for this model.102.2 tok/sデコード
〜$3,999 MSRP
Can Granite 3.1 8B run on NVIDIA A100 80GB?
YES — Runs Great
C51Usable
Estimated from fit model
Granite 3.1 8B needs ~16.0 GB VRAM. NVIDIA A100 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) — 16.0 GB, 112.0 tok/s, Runs well
16.0 GB required80.0 GB available
20% VRAM used
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
128K
Memory
16.0 GB / 80.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsGranite 3.1 8B on NVIDIA A100 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 | 128K |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 128K |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 128K |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 128K |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 128K |
Inference speed
Granite 3.1 8B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Granite 3.1 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 | 112.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 111.5 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 95.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.1 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 60.8 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 60.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 55.7 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 53.3 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 32.9 | Offloads |
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 3.1 8B (8B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C44 |
Q3_K_S | 3 | 3.9 GB | Low | C44 |
NVFP4 | 4 | 4.5 GB | Medium | C44 |
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 | C46 |
Get started
Copy-paste commands to run Granite 3.1 8B on your machine.
Run
ollama run granite3.1-denseアップグレードオプション
Granite 3.1 8Bを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA A100 80GB run Granite 3.1 8B?
Yes, NVIDIA A100 80GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 112.0 tok/s.
How much VRAM does Granite 3.1 8B need?
Granite 3.1 8B (8B parameters) requires approximately 16.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Granite 3.1 8B?
The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Granite 3.1 8B run at on NVIDIA A100 80GB?
On NVIDIA A100 80GB, Granite 3.1 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 A100 80GB run Granite 3.1 8B for coding?
For coding workloads, Granite 3.1 8B on NVIDIA A100 80GB receives a C grade with 112.0 tok/s and 128K context.
What context window can Granite 3.1 8B use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, Granite 3.1 8B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/granite-3.1-8b-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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