Can Codestral RAG 19B Pruned i1 run on Radeon Pro W7500 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~15.5 GB but Radeon Pro W7500 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 15.5 GB, exceeds 8.0 GB available

15.5 GB required8.0 GB available

194% VRAM needed

7.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.1 tok/s

TTFT

91295 ms

Safe context

Memory

15.5 GB / 8.0 GB

Offload

50%

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral RAG 19B Pruned i1 on Radeon Pro W7500 8GB
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: 2.1 tok/s decode · 91.3s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 15.5 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.5 tok/s	42574 ms	4K
Coding	F	Too heavy	2.1 tok/s	91295 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.1 tok/s	107894 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Codestral RAG 19B Pruned i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral RAG 19B Pruned i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~104 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	103.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	66.1	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	59.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	56.5	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	48.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	40.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	38.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	36.5	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.7	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	20.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	19.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	13.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	8.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.1	Too big

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 Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	F0
Q3_K_S	3	9.3 GB	Low	F0
NVFP4	4	10.6 GB	Medium	F0
Q4_K_M	4	11.6 GB	Medium	F0
Q5_K_M	5	13.7 GB	High	F0
Q6_K	6	15.6 GB	High	F0
Q8_0	8	20.3 GB	Very High	F0
F16	16	38.9 GB	Maximum	F0

Opções de upgrade

Hardware que roda bem Codestral RAG 19B Pruned i1

RX 7600 XT 16GBOpção econômica

16 GB VRAM (+8)288 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.10.4 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$329 MSRP

RX 9060 XT 16GBMelhor custo-benefício

16 GB VRAM (+8)320 GB/s (+96)

Makes the model fit on the accelerator instead of staying completely out of reach.12.5 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$349 MSRP

RX 9070 16GBUpgrade AMD

16 GB VRAM (+8)640 GB/s (+416)

Makes the model fit on the accelerator instead of staying completely out of reach.25.2 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$479 MSRP

RTX 5090 Laptop 24GBMaior salto

24 GB VRAM (+16)896 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.64.9 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

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

Frequently asked questions

Can Radeon Pro W7500 8GB run Codestral RAG 19B Pruned i1?

No, Codestral RAG 19B Pruned i1 requires more memory than Radeon Pro W7500 8GB provides.

How much VRAM does Codestral RAG 19B Pruned i1 need?

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 15.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral RAG 19B Pruned i1?

The recommended quantization for Codestral RAG 19B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral RAG 19B Pruned i1 run at on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Codestral RAG 19B Pruned i1 achieves approximately 2.1 tokens per second decode speed with a time-to-first-token of 91295ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on Radeon Pro W7500 8GB receives a F grade with 2.1 tok/s and 4K context.

What context window can Codestral RAG 19B Pruned i1 use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Codestral RAG 19B 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.

What should I upgrade first if Codestral RAG 19B Pruned i1 feels slow on Radeon Pro W7500 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for Radeon Pro W7500 8GB See all hardware for Codestral RAG 19B Pruned i1

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