Can HelpingAI2.5 10B i1 run on AMD Instinct MI100 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

HelpingAI2.5 10B i1 needs ~11.4 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~131 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) — 11.4 GB, 130.9 tok/s, Runs well

11.4 GB required32.0 GB available

36% VRAM used

Fit status

Runs well

Decode

130.9 tok/s

TTFT

1480 ms

Safe context

298K

Memory

11.4 GB / 32.0 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B 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: 130.9 tok/s decode · 1.5s TTFT (warm) · 327 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	130.9 tok/s	807 ms	298K
Coding	C	Runs well	130.9 tok/s	1480 ms	298K
Agentic Coding	C	Runs well	130.9 tok/s	2152 ms	298K
Reasoning	C	Runs well	130.9 tok/s	1749 ms	298K
RAG	C	Runs well	130.9 tok/s	2690 ms	298K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C43
Q3_K_S	3	4.9 GB	Low	C43
NVFP4	4	5.6 GB	Medium	C43
Q4_K_M	4	6.1 GB	Medium	C44
Q5_K_M	5	7.2 GB	High	C44
Q6_K	6	8.2 GB	High	C44
Q8_0	8	10.7 GB	Very High	C46
F16Best for your GPU	16	20.5 GB	Maximum	C49

Get started

Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server start

Frequently asked questions

Can AMD Instinct MI100 32GB run HelpingAI2.5 10B i1?

Yes, AMD Instinct MI100 32GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 130.9 tok/s.

How much VRAM does HelpingAI2.5 10B i1 need?

HelpingAI2.5 10B i1 (10B parameters) requires approximately 11.4 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 10B i1?

The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 10B i1 run at on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, HelpingAI2.5 10B i1 achieves approximately 130.9 tokens per second decode speed with a time-to-first-token of 1480ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on AMD Instinct MI100 32GB receives a C grade with 130.9 tok/s and 298K context.

What context window can HelpingAI2.5 10B i1 use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, HelpingAI2.5 10B i1 can safely use up to 298K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI100 32GB See all hardware for HelpingAI2.5 10B i1

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-5-10b-i1-gguf-on-instinct-mi100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

130.9 tok/s

807 ms

298K

Coding

Runs well

130.9 tok/s

1480 ms

298K

Agentic Coding

Runs well

130.9 tok/s

2152 ms

298K

Reasoning

Runs well

130.9 tok/s

1749 ms

298K

RAG

Runs well

130.9 tok/s

2690 ms

298K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C43
Q3_K_S	3	4.9 GB	Low	C43
NVFP4	4	5.6 GB	Medium	C43
Q4_K_M	4	6.1 GB	Medium	C44
Q5_K_M	5	7.2 GB	High	C44
Q6_K	6	8.2 GB	High	C44
Q8_0	8	10.7 GB	Very High	C46
F16Best for your GPU	16	20.5 GB	Maximum	C49

Frequently asked questions

Can AMD Instinct MI100 32GB run HelpingAI2.5 10B i1?

Yes, AMD Instinct MI100 32GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 130.9 tok/s.

How much VRAM does HelpingAI2.5 10B i1 need?

HelpingAI2.5 10B i1 (10B parameters) requires approximately 11.4 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 10B i1?

The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 10B i1 run at on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, HelpingAI2.5 10B i1 achieves approximately 130.9 tokens per second decode speed with a time-to-first-token of 1480ms using Q4_K_M quantization.

Can AMD Instinct MI100 32GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on AMD Instinct MI100 32GB receives a C grade with 130.9 tok/s and 298K context.

What context window can HelpingAI2.5 10B i1 use on AMD Instinct MI100 32GB?

On AMD Instinct MI100 32GB, HelpingAI2.5 10B i1 can safely use up to 298K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.