MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)
C
This setup is broadly balanced for this model.87.1 tok/s Dekodierung
ca. $2,499 MSRP
Can Granite 3.1 8B run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C54Usable
Estimated from fit model
Granite 3.1 8B needs ~10.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~96 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 10.9 GB, 95.7 tok/s, Runs well
10.9 GB required32.0 GB available
34% VRAM used
Fit status
Runs well
Decode
95.7 tok/s
TTFT
2024 ms
Safe context
128K
Memory
10.9 GB / 32.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsGranite 3.1 8B on Radeon AI PRO R9700 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: 95.7 tok/s decode · 2.0s TTFT (warm) · 239 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 | 95.7 tok/s | 1104 ms | 128K |
| Coding | C | Runs well | 95.7 tok/s | 2024 ms | 128K |
| Agentic Coding | B | Runs well | 95.7 tok/s | 2944 ms | 128K |
| Reasoning | C | Runs well | 95.7 tok/s | 2392 ms | 128K |
| RAG | B | Runs well | 95.7 tok/s | 3680 ms | 128K |
Quantization options
How Granite 3.1 8B (8B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C48 |
Q3_K_S | 3 | 3.9 GB | Low | C48 |
NVFP4 | 4 | 4.5 GB | Medium | C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C48 |
Q5_K_M | 5 | 5.8 GB | High | C49 |
Q6_K | 6 | 6.6 GB | High | C49 |
Q8_0 | 8 | 8.6 GB | Very High | C50 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C54 |
Get started
Copy-paste commands to run Granite 3.1 8B on your machine.
Run
ollama run granite3.1-denseUpgrade-Optionen
Hardware, die Granite 3.1 8B gut ausführt
Frequently asked questions
Can Radeon AI PRO R9700 32GB run Granite 3.1 8B?
Yes, Radeon AI PRO R9700 32GB can run Granite 3.1 8B with a C grade (Runs well). Expected decode speed: 95.7 tok/s.
How much VRAM does Granite 3.1 8B need?
Granite 3.1 8B (8B parameters) requires approximately 10.9 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 Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, Granite 3.1 8B achieves approximately 95.7 tokens per second decode speed with a time-to-first-token of 2024ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run Granite 3.1 8B for coding?
For coding workloads, Granite 3.1 8B on Radeon AI PRO R9700 32GB receives a C grade with 95.7 tok/s and 128K context.
What context window can Granite 3.1 8B use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, 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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