Intermediate 11 minBy VRAM

Which LLM for 16 GB of VRAM ?

16 GB of VRAM is the 2026 pro tier: RTX 4060 Ti 16GB, 5060 Ti 16GB, 4070 Ti Super, 5070 Ti, 5080, RX 7800 XT, RX 9070 XT. This is the threshold where Mistral Small 24B Q4 fits comfortably, where Devstral 24B and gpt-oss 20B unlock the coding agent, and where 32k+ context is practical. For serious/professional LLM use, this is the recommended target.

Choosing a machine? Our picks by budget →

By Mohamed Meguedmi·Update 2026-08-27·Tested on Windows, macOS, and Linux
Recommended hardware

A 16 GB card for this setup: RTX 5060 Ti 16GB (ASUS Prime).

Compare all options by budget, from €800 to €3,500 →

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#16 GB VRAM, the pro tier

→
The 24B–32B tier
16 GB unlocks Mistral Small 24B Q4 (14 GB) with room for context. It's the line between hobbyist and professional—beyond that, you have enough to serve a team with multi-stage RAG.

#1. GPUs covered

Budget (~€420)
RTX 4060 Ti 16GB used. Limited bandwidth (288 GB/s) but 16 GB.
Standard (~€500)
RTX 5060 Ti 16GB new. GDDR7, future-proof FP4.
Premium (~€750–950)
RTX 4070 Ti Super, RTX 5070 Ti.
Top (~€1,200–€1,300)
RTX 4080 Super, RTX 5080.
AMD
RX 7800 XT (~€430), 7900 GRE (~€500), 9070 XT (≈ €1,070 at the end of September 2026).

#2. Compatible models

16 GB VRAM — LLM models
ModelQuantVRAMOK?
Gemma 4 12BQ4_K_M7 GB★★★★★ comfortable
Granite 4.2 8BQ8_09 GB★★★★★
Qwen 3.5 9BQ8_011 GB★★★★★
Devstral Small 2 24BQ4_K_M14 GB★★★★ tight
Mistral Small 24BQ4_K_M14 GB★★★★ sweet spot
gpt-oss 20BQ4_K_M13 GB★★★★ tight

#3. Long context (32k+)

Qwen 3.5 9B Q5 + 32k
5.5 + 7 GB (standard KV cache) = 12.5 GB. Fits comfortably on 16 GB.
With Q8 KV cache
5.5 + 3.5 GB = 9 GB. 64k context is workable!
Gemma 4 12B Q4 + 16k
7.6 + 4 GB = 11.6 GB. Comfortable.

#4. Fine-tuning on 16 GB

QLoRA 7B-8B
10-12 GB required with batch 4 and 4096 context. Comfortable.
QLoRA 14B
12–15 GB required with batch 1–2. Possible with unsloth.
QLoRA 24B
16–18 GB with batch 1, 2048 context. Tight, nearly impossible.
Classic 7B LoRA
12–14 GB required. Tight but feasible.

#Frequently asked questions

What is the best LLM for 16 GB of VRAM?+
Mistral Small 24B Q4_K_M is the 2026 sweet spot—general-purpose and good in French. For coding agents: Devstral 24B Q4 (Apache 2.0). For speed and a 131k context: gpt-oss 20B (MXFP4).
Can 16 GB run a dense 70B model?+
Not comfortably. Q4 (42 GB) massively exceeds the limit, and Q2 IQ (21 GB) still exceeds it. For a serious local 70B, target 24 GB (RTX 3090/4090) or 32 GB (5090)—or a MoE such as Qwen 3.6 35B-A3B that activates only 3B and fits much better.
How many tokens/sec for Mistral Small 24B on 16 GB?+
Variable: RTX 4060 Ti 16GB = 18 tok/s, 5060 Ti 16GB = 22 tok/s, 4070 Ti Super = 28 tok/s, 5070 Ti = 32 tok/s, 5080 = 38 tok/s.
16 GB or 24 GB for an LLM?+
16 GB is enough for 95% of 2026 use cases (up to 24–32B). 24 GB opens up 70B with offloading + serious fine-tuning. If your budget is ≤ €1,500, get 16 GB. Above that, get 24 GB.
Is 16 GB enough for business use?+
For 1-2 users with simple RAG: yes. For 5+ concurrent users with batching: no, 24 GB+ recommended.

Prices change quickly: every Monday and Thursday, our tracker records the lowest price for local AI graphics cards, along with the price per GB of VRAM.

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