Can Codestral RAG 19B Pruned i1 run on RX 7900 XT 20GB?

YES — Tight Fit

C51Usable

Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~16.7 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced

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) — 16.7 GB, 41.4 tok/s, Tight fit

16.7 GB required20.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

41.4 tok/s

TTFT

4675 ms

Safe context

40K

Memory

16.7 GB / 20.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on RX 7900 XT 20GB
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: 41.4 tok/s decode · 4.7s TTFT (warm) · 104 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	41.4 tok/s	2550 ms	40K
Coding	C	Tight fit	41.4 tok/s	4675 ms	40K
Agentic Coding	C	Tight fit	41.4 tok/s	6799 ms	40K
Reasoning	C	Tight fit	41.4 tok/s	5525 ms	40K
RAG	C	Tight fit	41.4 tok/s	8499 ms	40K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C48
Q3_K_S	3	9.3 GB	Low	C50
NVFP4	4	10.6 GB	Medium	C50
Q4_K_M	4	11.6 GB	Medium	C50
Q5_K_M	5	13.7 GB	High	C50
Q6_KBest for your GPU	6	15.6 GB	High	C49
Q8_0	8	20.3 GB	Very High	F0
F16	16	38.9 GB	Maximum	F0

Get started

Copy-paste commands to run Codestral RAG 19B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-rag-19b-pruned-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral RAG 19B Pruned i1 well

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+4)960 GB/s (+160)

Raises estimated decode speed by about 44%.59.6 tok/s decode

Raises estimated decode speed by about 44%.

~$999 MSRP

AMD Instinct MI60 32GBBest value

32 GB VRAM (+12)1024 GB/s (+224)

Adds memory headroom for longer context windows and future model growth.43.3 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$8,999 MSRP

AMD Instinct MI100 32GBAMD upgrade

32 GB VRAM (+12)1228 GB/s (+428)

Raises estimated decode speed by about 66%.68.9 tok/s decode

Raises estimated decode speed by about 66%.

Adds memory headroom for longer context windows and future model growth.

~$11,500 MSRP

Frequently asked questions

Can RX 7900 XT 20GB run Codestral RAG 19B Pruned i1?

Yes, RX 7900 XT 20GB can run Codestral RAG 19B Pruned i1 with a C grade (Tight fit). Expected decode speed: 41.4 tok/s.

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

Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 16.7 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 RX 7900 XT 20GB?

On RX 7900 XT 20GB, Codestral RAG 19B Pruned i1 achieves approximately 41.4 tokens per second decode speed with a time-to-first-token of 4675ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Codestral RAG 19B Pruned i1 for coding?

For coding workloads, Codestral RAG 19B Pruned i1 on RX 7900 XT 20GB receives a C grade with 41.4 tok/s and 40K context.

What context window can Codestral RAG 19B Pruned i1 use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Codestral RAG 19B Pruned i1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for Codestral RAG 19B Pruned i1

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--codestral-rag-19b-pruned-i1-gguf-on-rx-7900-xt-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

41.4 tok/s

2550 ms

40K

Coding

Tight fit

41.4 tok/s

4675 ms

40K

Agentic Coding

Tight fit

41.4 tok/s

6799 ms

40K

Reasoning

Tight fit

41.4 tok/s

5525 ms

40K

RAG

Tight fit

41.4 tok/s

8499 ms

40K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C48
Q3_K_S	3	9.3 GB	Low	C50
NVFP4	4	10.6 GB	Medium	C50
Q4_K_M	4	11.6 GB	Medium	C50
Q5_K_M	5	13.7 GB	High	C50
Q6_KBest for your GPU	6	15.6 GB	High	C49
Q8_0	8	20.3 GB	Very High	F0
F16	16	38.9 GB	Maximum	F0