Can internlm2 5 1 8b chat i1 run on NVIDIA T4 16GB?

YES — Runs Great

C50Usable

Estimated from fit model

internlm2 5 1 8b chat i1 needs ~8.6 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 8.6 GB, 42.6 tok/s, Runs well

8.6 GB required16.0 GB available

54% VRAM used

Fit status

Runs well

Decode

42.6 tok/s

TTFT

4542 ms

Safe context

142K

Memory

8.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 5 1 8b chat i1 on NVIDIA T4 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: 42.6 tok/s decode · 4.5s TTFT (warm) · 107 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	42.6 tok/s	2478 ms	142K
Coding	C	Runs well	42.6 tok/s	4542 ms	142K
Agentic Coding	C	Runs well	42.6 tok/s	6607 ms	142K
Reasoning	C	Runs well	42.6 tok/s	5368 ms	142K
RAG	C	Runs well	42.6 tok/s	8258 ms	142K

Quantization options

How internlm2 5 1 8b chat i1 (8B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C47
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C48
Q5_K_M	5	5.8 GB	High	C49
Q6_K	6	6.6 GB	High	C50
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run internlm2 5 1 8b chat i1 on your machine.

Run

lms load hf-mradermacher--internlm2-5-1-8b-chat-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien internlm2 5 1 8b chat i1

RX 7900 XT 20GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 131%.98.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 131%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

RTX A4500 20GBOpción económica

20 GB VRAM (+4)640 GB/s (+320)

Sube la velocidad estimada de decodificación alrededor de un 140%.102.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 140%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,000 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run internlm2 5 1 8b chat i1?

Yes, NVIDIA T4 16GB can run internlm2 5 1 8b chat i1 with a C grade (Runs well). Expected decode speed: 42.6 tok/s.

How much VRAM does internlm2 5 1 8b chat i1 need?

internlm2 5 1 8b chat i1 (8B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 5 1 8b chat i1?

The recommended quantization for internlm2 5 1 8b chat i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 5 1 8b chat i1 run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, internlm2 5 1 8b chat i1 achieves approximately 42.6 tokens per second decode speed with a time-to-first-token of 4542ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run internlm2 5 1 8b chat i1 for coding?

For coding workloads, internlm2 5 1 8b chat i1 on NVIDIA T4 16GB receives a C grade with 42.6 tok/s and 142K context.

What context window can internlm2 5 1 8b chat i1 use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, internlm2 5 1 8b chat i1 can safely use up to 142K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA T4 16GB See all hardware for internlm2 5 1 8b chat i1

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