Can internlm2 math plus 20b i1 run on RX 7900 XT 20GB?

YES — Tight Fit

C51Usable

Estimated from fit model

internlm2 math plus 20b i1 needs ~17.4 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~39 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 17.4 GB, 39.3 tok/s, Tight fit

17.4 GB required20.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

39.3 tok/s

TTFT

4921 ms

Safe context

33K

Memory

17.4 GB / 20.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 20b i1 on RX 7900 XT 20GB
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: 39.3 tok/s decode · 4.9s TTFT (warm) · 98 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	39.3 tok/s	2684 ms	33K
Coding	C	Tight fit	39.3 tok/s	4921 ms	33K
Agentic Coding	C	Runs with offload	39.3 tok/s	7157 ms	33K
Reasoning	C	Tight fit	39.3 tok/s	5815 ms	33K
RAG	C	Runs with offload	39.3 tok/s	8947 ms	33K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C49
Q3_K_S	3	9.8 GB	Low	C50
NVFP4	4

Get started

Copy-paste commands to run internlm2 math plus 20b i1 on your machine.

Run

lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server start

Upgrade options

Hardware that runs internlm2 math plus 20b i1 well

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+4)960 GB/s (+160)

Raises estimated decode speed by about 44%.56.7 tok/s decode

Raises estimated decode speed by about 44%.

~$999 MSRP

AMD Instinct MI60 32GBBest value

32 GB VRAM (+12)1024 GB/s (+224)

Adds memory headroom for longer context windows and future model growth.41.1 tok/s decode

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

~$8,999 MSRP

AMD Instinct MI100 32GBAMD upgrade

32 GB VRAM (+12)1228 GB/s (+428)

Raises estimated decode speed by about 66%.65.4 tok/s decode

Raises estimated decode speed by about 66%.

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

~$11,500 MSRP

Frequently asked questions

Can RX 7900 XT 20GB run internlm2 math plus 20b i1?

Yes, RX 7900 XT 20GB can run internlm2 math plus 20b i1 with a C grade (Tight fit). Expected decode speed: 39.3 tok/s.

How much VRAM does internlm2 math plus 20b i1 need?

internlm2 math plus 20b i1 (20B parameters) requires approximately 17.4 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 math plus 20b i1?

The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 math plus 20b i1 run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, internlm2 math plus 20b i1 achieves approximately 39.3 tokens per second decode speed with a time-to-first-token of 4921ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on RX 7900 XT 20GB receives a C grade with 39.3 tok/s and 33K context.

What context window can internlm2 math plus 20b i1 use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, internlm2 math plus 20b i1 can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for internlm2 math plus 20b i1

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