Can Codestral 21B Pruned i1 run on Radeon PRO W7700 16GB?

Q: What should I upgrade first if Codestral 21B Pruned i1 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

D38Poor

Estimated from fit model

Codestral 21B Pruned i1 needs ~17.8 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~16 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) — 17.8 GB, 16.0 tok/s, Very compromised (needs ~1.3 GB host RAM)

17.8 GB required16.0 GB available

111% VRAM needed

1.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.3 GB host RAM)

Decode

16.0 tok/s

TTFT

12136 ms

Safe context

Memory

17.8 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.8 GB

KV Cache2.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodestral 21B Pruned i1 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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 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	C	Runs with offload (needs ~0.4 GB host RAM)	18.6 tok/s	5692 ms	4K
Coding	D	Very compromised (needs ~1.3 GB host RAM)	16.0 tok/s	12136 ms	4K
Agentic Coding	F	Too heavy	12.1 tok/s	23194 ms	4K
Reasoning	D	Very compromised	16.0 tok/s	14343 ms	4K
RAG	F	Too heavy	12.1 tok/s	28993 ms

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	C51
Q3_K_S	3	10.3 GB	Low	C50
NVFP4Best for your GPU

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 21B Pruned i1 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.37.5 tok/s decode

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

Raises estimated decode speed by about 134%.

~$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.54 tok/s decode

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

Raises estimated decode speed by about 238%.

~$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.29.5 tok/s decode

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

Raises estimated decode speed by about 84%.

~$1,899 MSRP

Frequently asked questions

Can Radeon PRO W7700 16GB run Codestral 21B Pruned i1?

Yes, Radeon PRO W7700 16GB can run Codestral 21B Pruned i1 with a D grade (Very compromised (needs ~1.3 GB host RAM)). Expected decode speed: 16.0 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 17.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 21B Pruned i1 run at on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Codestral 21B Pruned i1 achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12136ms using Q4_K_M quantization.

Can Radeon PRO W7700 16GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on Radeon PRO W7700 16GB receives a D grade with 16.0 tok/s and 4K context.

What context window can Codestral 21B Pruned i1 use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, Codestral 21B Pruned i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon PRO W7700 16GB See all hardware for Codestral 21B Pruned i1

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