RX 7900 XT 20GBコスパ最良
20 GB VRAM (+4)800 GB/s (+600)
B
Raises estimated decode speed by about 184%.112 tok/sデコード
Raises estimated decode speed by about 184%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can Granite 3.1 8B run on NVIDIA A2 16GB?
YES — Runs Great
B56Good
Estimated from fit model
Granite 3.1 8B needs ~9.3 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 9.3 GB, 39.5 tok/s, Runs well
9.3 GB required16.0 GB available
58% VRAM used
Fit status
Runs well
Decode
39.5 tok/s
TTFT
4899 ms
Safe context
71K
Memory
9.3 GB / 16.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsGranite 3.1 8B on NVIDIA A2 16GB
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: 39.5 tok/s decode · 4.9s TTFT (warm) · 99 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 | 36.8 tok/s | 2873 ms | 71K |
| Coding | B | Runs well | 36.8 tok/s | 5266 ms | 71K |
| Agentic Coding | B | Runs well | 36.8 tok/s | 7660 ms | 71K |
| Reasoning | B | Runs well | 36.8 tok/s | 6224 ms | 71K |
| RAG | B | Runs well | 36.8 tok/s | 9575 ms | 71K |
Quantization options
How Granite 3.1 8B (8B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C52 |
Q3_K_S | 3 | 3.9 GB | Low | C52 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_M | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | C54 |
Q6_K | 6 | 6.6 GB | High | C55 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | B56 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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 A2 16GB run Granite 3.1 8B?
Yes, NVIDIA A2 16GB can run Granite 3.1 8B with a B grade (Runs well). Expected decode speed: 36.8 tok/s.
How much VRAM does Granite 3.1 8B need?
Granite 3.1 8B (8B parameters) requires approximately 9.3 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 A2 16GB?
On NVIDIA A2 16GB, Granite 3.1 8B achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5266ms using Q4_K_M quantization.
Can NVIDIA A2 16GB run Granite 3.1 8B for coding?
For coding workloads, Granite 3.1 8B on NVIDIA A2 16GB receives a B grade with 36.8 tok/s and 71K context.
What context window can Granite 3.1 8B use on NVIDIA A2 16GB?
On NVIDIA A2 16GB, Granite 3.1 8B can safely use up to 71K 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-a2-16gb" 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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