MacBook Pro M4 Pro 24GBBudget pick
24 GB Unified (+8)
B
This setup is broadly balanced for this model.53.3 tok/s decode
~$1,999 MSRP
Can Granite 3.1 8B run on NVIDIA T4 16GB?
YES — Runs Great
B57Good
○Estimated from fit model
Granite 3.1 8B needs ~9.3 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~53 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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, 52.7 tok/s, Runs well
9.3 GB required16.0 GB available
58% VRAM used
Fit status
Runs well
Decode
52.7 tok/s
TTFT
3674 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 T4 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: 52.7 tok/s decode · 3.7s TTFT (warm) · 132 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 52.7 tok/s | 2004 ms | 71K |
| Coding | B | Runs well | 52.7 tok/s | 3674 ms | 71K |
| Agentic Coding | B | Runs well | 52.7 tok/s | 5344 ms | 71K |
| Reasoning | B | Runs well | 52.7 tok/s | 4342 ms | 71K |
| RAG | B | Runs well | 52.7 tok/s | 6680 ms | 71K |
Quantization options
How Granite 3.1 8B (8B params) fits at each quantization level on NVIDIA T4 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 |
Get started
Copy-paste commands to run Granite 3.1 8B on your machine.
Run
ollama run granite3.1-denseUpgrade options