Best Local LLM for 16GB VRAM (2026): gpt-oss 20B Wins

Bottom Line

• Best overall pick: gpt-oss 20B. 12.82 GiB of MXFP4 weights, 128K native context, and the smallest model we trust for unattended tool calling.
• The catch no other page states: its full 128K window does not fit. The KV cache costs 3.0 GiB at fp16, and 12.82 + 3.0 = 15.82 GiB on a 16 GiB card.
• The fix is one flag. Quantize the KV cache to Q8. That halves the cache to 1.5 GiB and the full window fits with room to spare.
• Best long-context pick: Qwen3.5 9B at Q8 — 11GB, 256K native context, and roughly 4 GiB of cache headroom left over.
• Do not buy 16GB for the 27B tier. Qwen3.6 27B is 17GB at q4_K_M and Qwen3.8 27B is 18GB. Both miss the card. That tier needs 24GB.

Ready to buy? See the tested hardware list with current prices.

The Question Is Not “Which Model” — It Is “At What Context”

Every other 16GB guide gives you a list of models and their file sizes. That list is not the answer, because a model’s file size is not what it occupies while running.

A running model needs two allocations: the weights, which are fixed, and the KV cache, which grows linearly with the context you feed it. On a 64GB card the cache is a rounding error. On a 16GB card it is the deciding factor.

gpt-oss 20B makes this concrete. The weights are 12.82 GiB. A 16 GiB card gives you roughly 15.0 to 15.5 GiB after the display buffer and the CUDA or ROCm context. So the model leaves you about 2.2 to 2.7 GiB. Whether the model “fits” depends entirely on whether your context fits in that gap.

Verified KV Cache Math for gpt-oss 20B

These figures come from the config.json in OpenAI’s own Hugging Face repo, read on 2026-09-07. They are not estimates.

The model has 24 layers in an alternating pattern. Twelve layers use full attention. Twelve use a sliding window of 128 tokens. Only the full-attention layers grow with context — the sliding layers stay pinned at 128 tokens and cost about 3 MiB in total, forever.

The per-token cost of the full-attention half:

12 layers x 2 (K and V) x 8 KV heads x 64 head_dim x 2 bytes (fp16)
  = 24,576 bytes
  = 24 KiB per token

Multiply that by the context length:

The last row is the finding. OpenAI ships gpt-oss 20B as a 128K-context model sized for a 16GB card, and both of those statements are true individually. They are not true at the same time.

The one-flag fix

Quantize the KV cache to Q8. It halves the cache to 1.5 GiB, which puts the full 131,072-token window at 14.3 GiB — the same footprint as 64K at fp16.

# llama.cpp — full window on a 16GB card
llama-server -m gpt-oss-20b.gguf \
  --ctx-size 131072 \
  --cache-type-k q8_0 --cache-type-v q8_0

We measured the quality cost of exactly this trade in KV cache quantization: Q8 vs Q4. Q8 is close to free. Q4 is not, and on a 16GB card you do not need to go there.

Top Picks for 16GB VRAM

1. gpt-oss 20B — the pick

12.82 GiB of MXFP4 weights (13,761,353,259 bytes across three safetensors files). Ollama lists the pull at 14GB. 128K native context. 32 experts with 4 active per token, so generation stays quick despite the 20B total.

It is the smallest model we trust to run an agent loop unattended. That single property is why 16GB remains a viable tier in 2026 — one gigabyte less and there would be no model here worth recommending.

Run it at 32K context and forget about it. Run it at 128K only with a Q8 cache.

2. Qwen3.5 9B at Q8 — the long-context pick

11GB, 256K native context. At Q8 you keep most of the quality of the full-precision model and still leave about 4 GiB for the cache, which is roughly double what gpt-oss 20B leaves you.

Pick this one when your workload is long documents rather than tool calling. The q4_K_M build is 6.6GB if you want even more room.

3. Qwen3.5 4B — the “run two things” pick

3.4GB at q4_K_M. Not the model you reach for alone. It is the model you load alongside something else, or the one you run when you want a fast draft model for speculative decoding.

What Does Not Fit, and Why It Matters

This is the part that should change a purchase decision.

Sizes read from the Ollama library on 2026-09-07.

Two curves crossed in 2026. The GPU market standardized on 16GB — the RTX 5060 Ti, the 5070 Ti, the 5080, and the RX 9070 XT all ship with exactly that. Meanwhile the best agentic models moved to 27B, which needs 17 to 18GB at four-bit.

16GB misses the current best tier by one to two gigabytes. That is the most expensive kind of miss, because no amount of quantization closes it without a real quality cost. If the 27B tier is what you want, you are shopping for 24GB, and our cheapest 32GB VRAM guide covers the tier above that.

Which 16GB Card

All 16GB cards hold the same models. The difference is the software stack and the price.

• RTX 5060 Ti 16GB — the cheapest new NVIDIA path. CUDA works with no configuration. See our full 5060 Ti guide.
• RTX 4060 Ti 16GB — the same capacity on last-generation silicon, covered in our 4060 Ti guide.
• RX 9070 XT 16GB — AMD’s RDNA4 card, listed as gfx1201 in AMD’s own ROCm 7.14 system requirements. That means official support, not an HSA_OVERRIDE_GFX_VERSION workaround.
• Arc B580 12GB — a tier down, not a 16GB card. Worth naming only because it is the cheapest way into local inference at all.

Prices across every one of these moved hard in 2026 and we are not going to quote a figure that ages badly. Check current listings.

See Also

• Is 16GB of VRAM still enough in 2026? — the yes-or-no version of this question, by workload
• How much VRAM do you need for 128K context? — the same cache arithmetic applied across model sizes
• KV cache quantization: Q8 vs Q4 — what the one-flag fix above actually costs you
• Lowest KV-cache models for long context — models whose architecture makes a 16GB card go further
• Best local LLM for 16GB RAM — the system-RAM question, which is a different machine
• Best local LLM for 64GB VRAM — the tier where the cache stops mattering