Can Codestral RAG 19B Pruned i1 run on AMD Instinct MI210 64GB?

YES — Runs Great

C48Usable

○Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~21.1 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~96 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 21.1 GB, 96.1 tok/s, Runs well

21.1 GB required64.0 GB available

33% VRAM used

Fit status

Runs well

Decode

96.1 tok/s

TTFT

2014 ms

Safe context

324K

Memory

21.1 GB / 64.0 GB

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsCodestral RAG 19B Pruned i1 on AMD Instinct MI210 64GB
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: 96.1 tok/s decode · 2.0s TTFT (warm) · 240 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	96.1 tok/s	1099 ms	324K
Coding	C	Runs well	96.1 tok/s	2014 ms	324K
Agentic Coding	C	Runs well	96.1 tok/s	2930 ms	324K
Reasoning	C	Runs well	96.1 tok/s	2381 ms	324K
RAG	C	Runs well	96.1 tok/s	3663 ms	324K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	C40
Q3_K_S	3	9.3 GB	Low	C41
NVFP4	4	10.6 GB	Medium	C41
Q4_K_M	4	11.6 GB	Medium	C41
Q5_K_M	5	13.7 GB	High	C41
Q6_K	6	15.6 GB	High	C42
Q8_0	8	20.3 GB	Very High	C43
F16Best for your GPU	16	38.9 GB	Maximum	C47

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

Frequently asked questions

See all results for AMD Instinct MI210 64GB See all hardware for Codestral RAG 19B Pruned i1

URL: https://willitrunai.com/can-run/hf-mradermacher--codestral-rag-19b-pruned-i1-gguf-on-instinct-mi210-64gb

⇱ Codestral RAG 19B Pruned i1 on AMD Instinct MI210 64GB? YES