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URL: https://willitrunai.com/can-run/hf-mradermacher--helpingai2-6b-i1-gguf-on-instinct-mi325x-256gb

⇱ HelpingAI2 6B i1 on AMD Instinct MI325X 256GB? YES

Can HelpingAI2 6B i1 run on AMD Instinct MI325X 256GB?

YES — Runs Great

C44Usable
○Estimated from fit model

HelpingAI2 6B i1 needs ~30.9 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~84 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) — 30.9 GB, 84.0 tok/s, Runs well
30.9 GB required256.0 GB available
12% VRAM used

Fit status

Runs well

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

5.1M

Memory

30.9 GB / 256.0 GB

Memory breakdown

Weights3.7 GB
KV Cache0.7 GB
Runtime0.9 GB
Headroom25.6 GB

See how fast it feels

See how fast it feelsHelpingAI2 6B i1 on AMD Instinct MI325X 256GB
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: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 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 well84.0 tok/s1257 ms5.1M
CodingCRuns well84.0 tok/s2305 ms5.1M
Agentic CodingCRuns well84.0 tok/s3352 ms5.1M
ReasoningCRuns well84.0 tok/s2724 ms5.1M
RAGCRuns well84.0 tok/s4190 ms5.1M

Quantization options

How HelpingAI2 6B i1 (6B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowD36
Q3_K_S
3
2.9 GB
LowD36
NVFP4
4
3.4 GB
MediumD36
Q4_K_M
4
3.7 GB
MediumD36
Q5_K_M
5
4.3 GB
HighD36
Q6_K
6
4.9 GB
HighD36
Q8_0
8
6.4 GB
Very HighD36
F16Best for your GPU
16
12.3 GB
MaximumD36

Get started

Copy-paste commands to run HelpingAI2 6B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-6b-i1-gguf && lms server start

Frequently asked questions

See all results for AMD Instinct MI325X 256GBSee all hardware for HelpingAI2 6B i1