willitrun·ai

Can Granite 4.1 8B run on Radeon PRO W7700 16GB?

YES — Runs Great

A79Great
Estimated from fit model

Granite 4.1 8B needs ~9.8 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~75 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) 9.8 GB, 74.9 tok/s, Runs well
9.8 GB required16.0 GB available
61% VRAM used

Fit status

Runs well

Decode

74.9 tok/s

TTFT

2586 ms

Safe context

56K

Memory

9.8 GB / 16.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on Radeon PRO W7700 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: 74.9 tok/s decode · 2.6s TTFT (warm) · 187 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well74.9 tok/s1411 ms56K
CodingARuns well74.9 tok/s2586 ms56K
Agentic CodingARuns well74.9 tok/s3762 ms56K
ReasoningARuns well74.9 tok/s3056 ms56K
RAGARuns well74.9 tok/s4702 ms56K

Inference speed

Granite 4.1 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite 4.1 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M112.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M112.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M112.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M112.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M112.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M112.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M102.2Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M96.9Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M83.3Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M82.6Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M82.6Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M52.9Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M52.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M48.5Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M42.6Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M23.8Heavy offload

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 4.1 8B (8B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowA71
Q3_K_S
3
3.9 GB
LowA72
NVFP4
4
4.5 GB
MediumA73
Q4_K_M
4
4.9 GB
MediumA73
Q5_K_M
5
5.8 GB
HighA74
Q6_K
6
6.6 GB
HighA75
Q8_0Best for your GPU
8
8.6 GB
Very HighA76
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Granite 4.1 8B on your machine.

Run

ollama run granite4.1:8b

Your hardware

More models your Radeon PRO W7700 16GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen 3.5 9B9BS66.5 tok/s
AlibabaQwen 3 14B14BS43 tok/s
MicrosoftPhi-4-reasoning-plus 14B14.7BS40.7 tok/s
OpenAIGPT-OSS 20B21BA39.3 tok/s
MistralMinistral 3 14B14BS42.8 tok/s

Frequently asked questions

Can Radeon PRO W7700 16GB run Granite 4.1 8B?

Yes, Radeon PRO W7700 16GB can run Granite 4.1 8B with a A grade (Runs well). Expected decode speed: 74.9 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 8B?

The recommended quantization for Granite 4.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 8B run at on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Granite 4.1 8B achieves approximately 74.9 tokens per second decode speed with a time-to-first-token of 2586ms using Q4_K_M quantization.

Can Radeon PRO W7700 16GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on Radeon PRO W7700 16GB receives a A grade with 74.9 tok/s and 56K context.

What context window can Granite 4.1 8B use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Granite 4.1 8B can safely use up to 56K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon PRO W7700 16GBSee all hardware for Granite 4.1 8B
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