Can HelpingAI 9B 200k i1 run on Radeon Pro W7800 32GB?

YES — Runs Great

C47Usable

Estimated from fit model

HelpingAI 9B 200k i1 needs ~10.6 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 10.6 GB, 61.9 tok/s, Runs well

10.6 GB required32.0 GB available

33% VRAM used

Fit status

Runs well

Decode

61.9 tok/s

TTFT

3128 ms

Safe context

340K

Memory

10.6 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsHelpingAI 9B 200k i1 on Radeon Pro W7800 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: 61.9 tok/s decode · 3.1s TTFT (warm) · 155 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	61.9 tok/s	1706 ms	340K
Coding	C	Runs well	61.9 tok/s	3128 ms	340K
Agentic Coding	C	Runs well	61.9 tok/s	4549 ms	340K
Reasoning	C	Runs well	61.9 tok/s	3696 ms	340K
RAG	C	Runs well	61.9 tok/s	5686 ms	340K

Quantization options

How HelpingAI 9B 200k i1 (9B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C43
Q3_K_S	3	4.4 GB	Low	C43
NVFP4	4	5.0 GB	Medium	C43
Q4_K_M	4	5.5 GB	Medium	C43
Q5_K_M	5	6.5 GB	High	C44
Q6_K	6	7.4 GB	High	C44
Q8_0	8	9.6 GB	Very High	C45
F16Best for your GPU	16	18.5 GB	Maximum	C49

Get started

Copy-paste commands to run HelpingAI 9B 200k i1 on your machine.

Run

lms load hf-mradermacher--helpingai-9b-200k-i1-gguf && lms server start

升级选项

能流畅运行 HelpingAI 9B 200k i1 的硬件

MacBook Pro M4 Max 48GB经济之选

48 GB Unified (+16)

This setup is broadly balanced for this model.68.3 tok/s 解码

~$2,499 MSRP

Mac Studio M2 Ultra 64GB最佳性价比

64 GB Unified (+32)800 GB/s (+224)

Raises estimated decode speed by about 37%.84.5 tok/s 解码

Raises estimated decode speed by about 37%.

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

~$3,999 MSRP

Frequently asked questions

Can Radeon Pro W7800 32GB run HelpingAI 9B 200k i1?

Yes, Radeon Pro W7800 32GB can run HelpingAI 9B 200k i1 with a C grade (Runs well). Expected decode speed: 61.9 tok/s.

How much VRAM does HelpingAI 9B 200k i1 need?

HelpingAI 9B 200k i1 (9B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI 9B 200k i1?

The recommended quantization for HelpingAI 9B 200k i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI 9B 200k i1 run at on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, HelpingAI 9B 200k i1 achieves approximately 61.9 tokens per second decode speed with a time-to-first-token of 3128ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run HelpingAI 9B 200k i1 for coding?

For coding workloads, HelpingAI 9B 200k i1 on Radeon Pro W7800 32GB receives a C grade with 61.9 tok/s and 340K context.

What context window can HelpingAI 9B 200k i1 use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, HelpingAI 9B 200k i1 can safely use up to 340K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7800 32GB See all hardware for HelpingAI 9B 200k i1

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