Will It Run AI

Can stabilityai japanese stablelm instruct beta 70b run on AMD Instinct MI210 64GB?

YES — Tight Fit

C49Usable
Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~58.2 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 58.2 GB, 26.1 tok/s, Tight fit
58.2 GB required64.0 GB available
91% VRAM used

Fit status

Tight fit

Decode

26.1 tok/s

TTFT

7422 ms

Safe context

27K

Memory

58.2 GB / 64.0 GB

Memory breakdown

Weights42.7 GB
KV Cache8.2 GB
Runtime0.9 GB
Headroom6.4 GB

See how fast it feels

See how fast it feelsstabilityai japanese stablelm instruct beta 70b on AMD Instinct MI210 64GB
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: 26.1 tok/s decode · 7.4s TTFT (warm) · 65 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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit26.1 tok/s4048 ms27K
CodingCTight fit26.1 tok/s7422 ms27K
Agentic CodingCRuns with offload (needs ~1.5 GB host RAM)18.1 tok/s15557 ms27K
ReasoningCTight fit26.1 tok/s8771 ms27K
RAGCRuns with offload (needs ~1.5 GB host RAM)18.1 tok/s19446 ms27K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowC46
Q3_K_S
3
34.3 GB
LowC47
NVFP4
4
39.2 GB
MediumC47
Q4_K_M
4
42.7 GB
MediumC47
Q5_K_MBest for your GPU
5
50.4 GB
HighC47
Q6_K
6
57.4 GB
HighF0
Q8_0
8
74.9 GB
Very HighF0
F16
16
143.5 GB
MaximumF0

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

アップグレードオプション

stabilityai japanese stablelm instruct beta 70bを快適に動かすハードウェア

AMD Instinct MI300A 128GBコスパ重視
128 GB VRAM (+64)5300 GB/s (+3662)
C
Raises estimated decode speed by about 233%.86.9 tok/sデコード

Raises estimated decode speed by about 233%.

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

〜$12,000 MSRP

AMD Instinct MI250X 128GBコスパ最良
128 GB VRAM (+64)3200 GB/s (+1562)
C
Raises estimated decode speed by about 124%.58.5 tok/sデコード

Raises estimated decode speed by about 124%.

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

〜$15,000 MSRP

AMD Instinct MI250 128GBAMDアップグレード
128 GB VRAM (+64)3200 GB/s (+1562)
C
Raises estimated decode speed by about 95%.51 tok/sデコード

Raises estimated decode speed by about 95%.

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

〜$19,000 MSRP

Frequently asked questions

Can AMD Instinct MI210 64GB run stabilityai japanese stablelm instruct beta 70b?

Yes, AMD Instinct MI210 64GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Tight fit). Expected decode speed: 26.1 tok/s.

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

stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 58.2 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 MI210 64GB?

On AMD Instinct MI210 64GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 26.1 tokens per second decode speed with a time-to-first-token of 7422ms using Q4_K_M quantization.

Can AMD Instinct MI210 64GB run stabilityai japanese stablelm instruct beta 70b for coding?

For coding workloads, stabilityai japanese stablelm instruct beta 70b on AMD Instinct MI210 64GB receives a C grade with 26.1 tok/s and 27K context.

What context window can stabilityai japanese stablelm instruct beta 70b use on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 27K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI210 64GBSee all hardware for stabilityai japanese stablelm instruct beta 70b
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