Can internlm2 math plus 20b i1 run on RX 9060 XT 16GB?

YES — With Offload

D37Poor

Estimated from fit model

internlm2 math plus 20b i1 needs ~17.0 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 17.0 GB, 10.8 tok/s, Runs with offload (needs ~0.7 GB host RAM)

17.0 GB required16.0 GB available

106% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.7 GB host RAM)

Decode

10.8 tok/s

TTFT

17844 ms

Safe context

Memory

17.0 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 math plus 20b i1 on RX 9060 XT 16GB
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: 10.8 tok/s decode · 17.8s TTFT (warm) · 27 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	16.5 tok/s	6390 ms	9K
Coding	D	Runs with offload (needs ~0.7 GB host RAM)	10.8 tok/s	17844 ms	9K
Agentic Coding	F	Too heavy	8.3 tok/s	34041 ms	9K
Reasoning	D	Runs with offload (needs ~0.7 GB host RAM)	10.8 tok/s	21088 ms	9K
RAG	F	Too heavy	8.3 tok/s	42551 ms	9K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C51
Q3_K_S	3	9.8 GB	Low	C51
NVFP4	4	11.2 GB	Medium	C50
Q4_K_MBest for your GPU	4	12.2 GB	Medium	C50
Q5_K_M	5	14.4 GB	High	F0
Q6_K	6	16.4 GB	High	F0
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0

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-Optionen

Hardware, die internlm2 math plus 20b i1 gut ausführt

RX 7900 XT 20GBGünstige Wahl

20 GB VRAM (+4)800 GB/s (+480)

Removes host-memory offload, which is usually the single biggest latency and throughput win.39.3 tok/s Dekodierung

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 264%.

ca. $899 MSRP

RX 7900 XTX 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+8)960 GB/s (+640)

Removes host-memory offload, which is usually the single biggest latency and throughput win.56.7 tok/s Dekodierung

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 425%.

ca. $999 MSRP

Radeon AI PRO R9700 32GBAMD-Upgrade

32 GB VRAM (+16)640 GB/s (+320)

Removes host-memory offload, which is usually the single biggest latency and throughput win.31 tok/s Dekodierung

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 187%.

ca. $1,899 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run internlm2 math plus 20b i1?

Yes, RX 9060 XT 16GB can run internlm2 math plus 20b i1 with a D grade (Runs with offload (needs ~0.7 GB host RAM)). Expected decode speed: 10.8 tok/s.

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

internlm2 math plus 20b i1 (20B parameters) requires approximately 17.0 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 9060 XT 16GB?

On RX 9060 XT 16GB, internlm2 math plus 20b i1 achieves approximately 10.8 tokens per second decode speed with a time-to-first-token of 17844ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on RX 9060 XT 16GB receives a D grade with 10.8 tok/s and 9K context.

What context window can internlm2 math plus 20b i1 use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, internlm2 math plus 20b i1 can safely use up to 9K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm2 math plus 20b i1 feels slow on RX 9060 XT 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RX 9060 XT 16GB See all hardware for internlm2 math plus 20b i1

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