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URL: https://willitrunai.com/can-run/hf-mradermacher--helply-10-2b-chat-i1-gguf-on-instinct-mi100-32gb

⇱ Helply 10.2b chat i1 on AMD Instinct MI100 32GB? YES

Can Helply 10.2b chat i1 run on AMD Instinct MI100 32GB?

YES — Runs Great

C49Usable
○Estimated from fit model

Helply 10.2b chat i1 needs ~11.5 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~128 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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) — 11.5 GB, 128.3 tok/s, Runs well
11.5 GB required32.0 GB available
36% VRAM used

Fit status

Runs well

Decode

128.3 tok/s

TTFT

1509 ms

Safe context

290K

Memory

11.5 GB / 32.0 GB

Memory breakdown

Weights6.2 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat i1 on AMD Instinct MI100 32GB
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: 128.3 tok/s decode · 1.5s TTFT (warm) · 321 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
ChatCRuns well128.3 tok/s823 ms290K
CodingCRuns well128.3 tok/s1509 ms290K
Agentic CodingCRuns well128.3 tok/s2195 ms290K
ReasoningCRuns well128.3 tok/s1784 ms290K
RAGCRuns well128.3 tok/s2744 ms290K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.0 GB
LowC43
Q3_K_S
3
5.0 GB
LowC43
NVFP4
4
5.7 GB
MediumC43
Q4_K_M
4
6.2 GB
MediumC44
Q5_K_M
5
7.3 GB
HighC44
Q6_K
6
8.4 GB
HighC44
Q8_0
8
10.9 GB
Very HighC46
F16Best for your GPU
16
20.9 GB
MaximumC49

Get started

Copy-paste commands to run Helply 10.2b chat i1 on your machine.

Run

lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server start

Frequently asked questions

See all results for AMD Instinct MI100 32GBSee all hardware for Helply 10.2b chat i1