Can stabilityai japanese stablelm instruct beta 70b run on AMD Instinct MI350X 288GB?

YES — Runs Great

C47Usable

Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~80.6 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~137 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) — 80.6 GB, 136.8 tok/s, Runs well

80.6 GB required288.0 GB available

28% VRAM used

Fit status

Runs well

Decode

136.8 tok/s

TTFT

1416 ms

Safe context

421K

Memory

80.6 GB / 288.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsstabilityai   japanese stablelm instruct beta 70b on AMD Instinct MI350X 288GB
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: 136.8 tok/s decode · 1.4s TTFT (warm) · 342 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	136.8 tok/s	772 ms	421K
Coding	C	Runs well	136.8 tok/s	1416 ms	421K
Agentic Coding	C	Runs well	136.8 tok/s	2059 ms	421K
Reasoning	C	Runs well	136.8 tok/s	1673 ms	421K
RAG	C	Runs well	136.8 tok/s	2574 ms	421K

Inference speed

stabilityai japanese stablelm instruct beta 70b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for stabilityai japanese stablelm instruct beta 70b at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	14.6	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	10.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	9.9	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	7.7	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	7.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	7.0	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	6.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	4.7	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	3.6	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.7	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.4	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.3	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	D37
Q3_K_S	3	34.3 GB	Low	D37
NVFP4	4	39.2 GB	Medium	D38
Q4_K_M	4	42.7 GB	Medium	D38
Q5_K_M	5	50.4 GB	High	D39
Q6_K	6	57.4 GB	High	D39
Q8_0	8	74.9 GB	Very High	C41
F16Best for your GPU	16	143.5 GB	Maximum	C46

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

Can AMD Instinct MI350X 288GB run stabilityai japanese stablelm instruct beta 70b?

Yes, AMD Instinct MI350X 288GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 136.8 tok/s.

How much VRAM does stabilityai japanese stablelm instruct beta 70b need?

stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 80.6 GB of memory with Q4_K_M quantization.

What is the best quantization for stabilityai japanese stablelm instruct beta 70b?

The recommended quantization for stabilityai japanese stablelm instruct beta 70b is Q4_K_M, which balances quality and memory efficiency.

What speed will stabilityai japanese stablelm instruct beta 70b run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 136.8 tokens per second decode speed with a time-to-first-token of 1416ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run stabilityai japanese stablelm instruct beta 70b for coding?

For coding workloads, stabilityai japanese stablelm instruct beta 70b on AMD Instinct MI350X 288GB receives a C grade with 136.8 tok/s and 421K context.

What context window can stabilityai japanese stablelm instruct beta 70b use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 421K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for stabilityai japanese stablelm instruct beta 70b

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