Can stabilityai japanese stablelm instruct beta 70b run on AMD Instinct MI300A 128GB?

YES — Runs Great

C52Usable

○Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~64.6 GB VRAM. AMD Instinct MI300A 128GB has 128.0 GB. With Q4_K_M quantization, expect ~87 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) — 64.6 GB, 86.9 tok/s, Runs well

64.6 GB required128.0 GB available

50% VRAM used

Fit status

Runs well

Decode

86.9 tok/s

TTFT

2228 ms

Safe context

140K

Memory

64.6 GB / 128.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsstabilityai japanese stablelm instruct beta 70b on AMD Instinct MI300A 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 86.9 tok/s decode · 2.2s TTFT (warm) · 217 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	86.9 tok/s	1215 ms	140K
Coding	C	Runs well	86.9 tok/s	2228 ms	140K
Agentic Coding	C	Runs well	86.9 tok/s	3241 ms	140K
Reasoning	C	Runs well	86.9 tok/s	2633 ms	140K
RAG	C	Runs well	86.9 tok/s	4051 ms	140K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on AMD Instinct MI300A 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	D40
Q3_K_S	3	34.3 GB	Low	C41
NVFP4	4	39.2 GB	Medium	C42
Q4_K_M	4	42.7 GB	Medium	C43
Q5_K_M	5	50.4 GB	High	C44
Q6_K	6	57.4 GB	High	C45
Q8_0Best for your GPU	8	74.9 GB	Very High	C47
F16	16	143.5 GB	Maximum	F0

Get started

Copy-paste commands to run stabilityai japanese stablelm instruct beta 70b on your machine.

Run

lms load hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf && lms server start

Frequently asked questions

See all results for AMD Instinct MI300A 128GB See all hardware for stabilityai japanese stablelm instruct beta 70b

URL: https://willitrunai.com/can-run/hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf-on-instinct-mi300a-128gb

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