Best local LLMs for tool use

Run open models capable of function calling, designed for agents and apps that use tools and APIs. We rank them using the llmrun Composite Score, offering the top choice for every memory capacity.

Best for tool use on

8 GB 12 GB 16 GB 24 GB 32 GB 48 GB+

Top pick

Qwen3.8 27B

Alibaba · Q4_K_M · 17.9 GB · up to 64K context · on RTX 4090

Score
Speed: 37.6 t/s
Grade: A

How to run →

All 204 tool-use models for 24 GB ↓

Also worth a look

Gemma 4 31B IT

Q4_K_M · 21.7 GB · 32.1 t/s · 32K ctx

Qwen3.6 27B

Q4_K_M · 17.9 GB · 37.6 t/s · 64K ctx

Also worth a look

Gemma 4 31B IT

Q4_K_M · 21.7 GB · 32.1 t/s · 32K ctx

Best tool-use model by memory

Highest llmrun Composite Score that fits each size with at least 32K context and 15 t/s, at a Q3 or better quant. Speed is decode tokens per second on that size's reference GPU. The last row has no memory limit: the best model whatever it needs, on the GPU plan shown. Full ranking

8 GB · RTX 5060
Qwen3.5 9B45.8 t/s · 32K ctx
Q4_K_M · 6.6 GB
Runner-up: Gemma 4 E4B IT
12 GB · RTX 5070
Qwen3.5 9B68.7 t/s · 128K ctx
Q4_K_M · 6.6 GB
Runner-up: GPT OSS 20B
16 GB · RTX 5080
Qwen3.8 27B42.6 t/s · 32K ctx
Q4_0 · 15.1 GB
Runner-up: Gemma 4 26B A4B IT
24 GB · RTX 4090
Qwen3.8 27B37.6 t/s · 64K ctx
Q4_K_M · 17.9 GB
Runner-up: Gemma 4 31B IT
32 GB · RTX 5090
Qwen3.8 27B66.9 t/s · 256K ctx
Q4_K_M · 17.9 GB
Runner-up: Gemma 4 31B IT
48 GB+ · no limit · about 5× H200 SXM 141 GB
MiMo V2.6 Pro RL1M ctx
Q4_K_M · needs 614 GB
Runner-up: GLM 5.3

Best tool-use model for each memory size
Memory	Best pick	Speed	Context	Runner-up
8 GBRTX 5060	Qwen3.5 9BQ4_K_M · 6.6 GB	45.8 t/s	32K	Gemma 4 E4B IT54.7 t/s
12 GBRTX 5070	Qwen3.5 9BQ4_K_M · 6.6 GB	68.7 t/s	128K	GPT OSS 20B96.4 · 156.0 t/s
16 GBRTX 5080	Qwen3.8 27BQ4_0 · 15.1 GB	42.6 t/s	32K	Gemma 4 26B A4B IT105.8 · 178.1 t/s
24 GBRTX 4090	Qwen3.8 27BQ4_K_M · 17.9 GB	37.6 t/s	64K	Gemma 4 31B IT106.6 · 32.1 t/s
32 GBRTX 5090	Qwen3.8 27BQ4_K_M · 17.9 GB	66.9 t/s	256K	Gemma 4 31B IT106.6 · 57.1 t/s
48 GB+ · no limitabout 5× H200 SXM 141 GB	MiMo V2.6 Pro RLQ4_K_M · needs 614 GB	n/a	1M	GLM 5.3130.1

Tool-use models that run on 24 GB204

Ranked by Composite Score, models without one after. Best quant that fits with a 16K context; speed is decode tokens per second on the reference GPU.

204 models · 165 run great · 14 run well

Featured only

Score: llmrun Score, 100 = average model. Fit: how well it runs on this hardware, S best to F won't fit. Ordered by Score, recency and Fit. How ranking works

Showing compatibility for NVIDIA GeForce RTX 4090

LLM models compatible with NVIDIA GeForce RTX 4090 — ranked by Score, recency and Fit
Model	Quant	VRAM	Speed	Context	Fit
Hy MT2 30B A3B30.1BTranslationFunctions Q4_K_M·170.3 t/s	Q4_K_M	19.1 GB80%	170.3 t/s	262K	S98, Runs great
Granite Vision 4.1 4B4.0BVisionFunctions Q4_K_M·228.3 t/s	Q4_K_M	4.0 GB17%	228.3 t/s	131K	S100, Runs great
MiniCPM V 4.61.3BVisionFunctions Q4_K_M·579.8 t/s	Q4_K_M	1.5 GB6%	579.8 t/s	262K	S100, Runs great
MiniCPM V 4.6 Thinking1.3BVisionFunctions Q4_K_M·579.8 t/s	Q4_K_M	1.5 GB6%	579.8 t/s	262K	S100, Runs great
JiRackDeltaNet 27B27.3BChatFunctions Q4_K_M·38.2 t/s	Q4_K_M	17.6 GB73%	38.2 t/s	262K	A74, Runs well
JiRackUltra 1B1.8BChatFunctions Q4_K_M·461.4 t/s	Q4_K_M	1.8 GB8%	461.4 t/s	131K	S100, Runs great
Ornith 1.0 9B9.4BChatFunctions Q4_K_M·105.5 t/s	Q4_K_M	6.5 GB27%	105.5 t/s	262K	S92, Runs great
DeepSeek R1 0528 Qwen3 8B8.2BChatReasoningFunctions Q4_K_M·118.7 t/s	Q4_K_M	7.6 GB32%	118.7 t/s	131K	S95, Runs great
JiRackUltra 14B14.8BChatFunctions Q4_K_M·68.5 t/s	Q4_K_M	12.4 GB52%	68.5 t/s	131K	S85, Runs great
Llm Jp 4.1 32B A3b Thinking32.1BChatFunctions Q4_K_M·187.6 t/s	Q4_K_M	20.1 GB84%	187.6 t/s	66K	S94, Runs great
NVIDIA Nemotron Labs Dory1.0 4B BF164.0BChatFunctions Q4_K_M·214.1 t/s	Q4_K_M	5.7 GB24%	214.1 t/s	262K	S100, Runs great
LFM2.5 VL 450M449MVisionFunctions Q4_K_M·1023.8 t/s	Q4_K_M	0.8 GB3%	1023.8 t/s	128K	S100, Runs great
ZDTaichu5.0 9B9.8BVisionFunctions Q4_K_M·101.4 t/s	Q4_K_M	9.2 GB38%	101.4 t/s	—	S92, Runs great
Chandra8.8BVisionFunctions Q4_K_M·111.8 t/s	Q4_K_M	8.0 GB33%	111.8 t/s	262K	S94, Runs great
ALoDLM 8B8.2BChatCodeFunctions Q4_K_M·118.7 t/s	Q4_K_M	7.6 GB32%	118.7 t/s	41K	S95, Runs great

How much memory do you need for a tool-use model?

From 8 to 12 GB the best tool-use model is Qwen3.5 9B (score 99). From 16 to 32 GB it is Qwen3.8 27B (score 122). With no memory limit, the best is MiMo V2.6 Pro RL (score 133). Since agents pass lengthy prompts packed with tool definitions and outcomes, both context window size and processing speed matter. Choose a model that retains a generous amount of context within its memory.

Frequently Asked Questions

What is the best local LLM for tool use?

On 24 GB, Qwen3.8 27B is the top pick with a score of 122, running at about 38 t/s on RTX 4090. It fits up to 64K tokens of context there. On 8 GB, the best fit is Qwen3.5 9B. With no memory limit, the best is MiMo V2.6 Pro RL. Open a model to see which GPUs and Macs run it.

Can I run a tool-use model on a laptop or an 8 GB GPU?

Yes. On 8 GB the best pick is Qwen3.5 9B (Q4_K_M, 6.6 GB), which runs at about 46 t/s on RTX 5060. Laptops with 8 to 16 GB of memory can run smaller models in the same way.

What is tool calling?

Instead of plain text, the model outputs a structured call like a function name plus its arguments. Your code executes that function, sends back the result, and lets the model continue from there.

Which runtime supports tool calling for local models?

Ollama, LM Studio, llama.cpp, and vLLM all offer an OpenAI-compatible tools interface for models built to use it. Remember that the model's chat template needs to support these tools.