Can Granite Code 20B run on Radeon PRO W7700 16GB?

Q: What should I upgrade first if Granite Code 20B feels slow on Radeon PRO W7700 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

B68Good

Estimated from fit model

Granite Code 20B needs ~17.9 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 17.9 GB, 17.9 tok/s, Very compromised (needs ~1.3 GB host RAM)

17.9 GB required16.0 GB available

112% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.3 GB host RAM)

Decode

17.9 tok/s

TTFT

10833 ms

Safe context

Memory

17.9 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite Code 20B 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: 17.9 tok/s decode · 10.8s TTFT (warm) · 45 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.2 GB host RAM)	21.7 tok/s	4859 ms	7K
Coding	B	Very compromised (needs ~1.3 GB host RAM)	17.9 tok/s	10833 ms	7K
Agentic Coding	F	Too heavy	12.7 tok/s	22228 ms	7K
Reasoning	B	Very compromised (needs ~1.3 GB host RAM)	17.9 tok/s	12803 ms	7K
RAG	F	Too heavy	12.7 tok/s	27785 ms

Inference speed

Granite Code 20B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite Code 20B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~106 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	106.3	Fits
RTX 4090 24GB	24 GB	Q4_K_M	67.8	Fits

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	A81
Q3_K_S	3	9.8 GB	Low	A81
NVFP4	4

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Upgrade options

Hardware that runs Granite Code 20B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+224)

Removes host-memory offload, which is usually the single biggest latency and throughput win.42.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 137%.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+384)

Removes host-memory offload, which is usually the single biggest latency and throughput win.61.2 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 242%.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+64)

Removes host-memory offload, which is usually the single biggest latency and throughput win.33.4 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 87%.

~$1,899 MSRP

Frequently asked questions

Can Radeon PRO W7700 16GB run Granite Code 20B?

Yes, Radeon PRO W7700 16GB can run Granite Code 20B with a B grade (Very compromised (needs ~1.3 GB host RAM)). Expected decode speed: 17.9 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 17.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Granite Code 20B achieves approximately 17.9 tokens per second decode speed with a time-to-first-token of 10833ms using Q4_K_M quantization.

Can Radeon PRO W7700 16GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on Radeon PRO W7700 16GB receives a B grade with 17.9 tok/s and 7K context.

What context window can Granite Code 20B use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Granite Code 20B can safely use up to 7K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on Radeon PRO W7700 16GB?

See all results for Radeon PRO W7700 16GB See all hardware for Granite Code 20B

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