Overview
16GB VRAM is enough for local LLMs up to about 14B parameters at 4-bit to 6-bit with a long-ish context, and it only just holds a 27B at Q3_K_M or lower; a 32B at Q4_K_M and any 70B do not fit. The deciding arithmetic is the one on methodology and vram-requirements: weights_GB = params x bits / 8 / 1e9, plus KV cache, plus an assumed 1 to 2 GB overhead. Tokens per second are unknown on this page: none of the vendor pages read here publish a figure for these cards.
How do you check whether a model fits in 16GB?
Add three pieces in decimal GB (bytes divided by 1e9):
- weights_GB = params x bits / 8 / 1e9
- KV cache bytes = 2 x layers x kv_heads x head_dim x context x bytes_per_value
- overhead = an assumed 1 to 2 GB (an assumption, not a measurement)
Bits per weight come from the llama.cpp quantize README (Llama 3.1 8B table): Q2_K 3.1593, Q3_K_M 3.9960, Q4_K_M 4.8944, Q5_K_M 5.7036, Q6_K 6.5633, Q8_0 8.5008. Using them for other models is an estimate; a real GGUF file can differ. If a total lands within 1 GB of 16, this page does not call it a clean fit. Background on quants: llm-quantization-explained.
What LLM can I run with 16GB VRAM?
Models up to 14B at Q4_K_M through Q6_K. Totals at 4,096 tokens, using the Qwen2.5 model cards (14B: 14.7B parameters, 48 layers; 32B: 32.5B, 64 layers) and each config.json (8 KV heads, hidden size 5120 over 40 query heads gives head dimension 128, bfloat16 so 2 bytes per value):
| Model and quant | Weights (GB) | KV at 4,096 (GB) | Total with 1 to 2 GB overhead (GB) | Verdict on 16 GB |
|---|---|---|---|---|
| Qwen2.5-14B Q4_K_M | 8.9935 | 0.8053 | 10.7988 to 11.7988 | Fits |
| Qwen2.5-14B Q6_K | 12.0601 | 0.8053 | 13.8654 to 14.8654 | Fits |
| Qwen2.5-14B Q8_0 | 15.6202 | 0.8053 | 17.4255 to 18.4255 | Does not fit |
| Qwen2.5-14B Q4_K_M at 16,384 tokens | 8.9935 | 3.2212 | 13.2147 to 14.2147 | Fits |
| Qwen2.5-14B Q4_K_M at 32,768 tokens | 8.9935 | 6.4425 | 16.4360 to 17.4360 | Does not fit |
| Qwen2.5-32B Q4_K_M | 19.8835 | 1.0737 | 21.9572 to 22.9572 | Does not fit |
The same method puts a 7B (Qwen2.5-7B, 7.61B parameters) at 5.8907 to 6.8907 GB at Q4_K_M, see vram-requirements. For the 12 GB tier see 12gb-vram-llm; for 24 GB see 24gb-vram-llm; for the three side by side see 24gb-vs-16gb-vs-12gb-vram.
What is the best setup for Qwen3.8 27B on 16GB VRAM?
Q2_K fits with margin; Q3_K_M is borderline; Q4_K_M and above do not. The Qwen3.8-27B card states “27B” parameters (the Hub metadata lists 28B; this page uses the card’s 27B), 64 layers in 16 blocks of three Gated DeltaNet layers then one Gated Attention layer, with 4 KV heads and head dimension 256 in the attention layers, and a 262,144-token native context. Its config.json lists 16 full_attention layers and bfloat16. Weights: 27e9 x 4.8944 / 8 / 1e9 = 16.5186 GB at Q4_K_M, which already exceeds 16 GB.
Assumption: only the 16 full-attention layers hold a KV cache, so KV = 2 x 16 x 4 x 256 x context x 2 bytes. The memory the linear-attention layers need is unknown here, and so is the quality cost of low quants, because no source read states one.
| Quant | Weights (GB) | KV at 4,096 (GB) | Total with 1 to 2 GB overhead (GB) | Verdict on 16 GB |
|---|---|---|---|---|
| Q2_K | 10.6626 | 0.2684 | 11.9310 to 12.9310 | Fits on paper |
| Q3_K_M | 13.4865 | 0.2684 | 14.7549 to 15.7549 | Borderline, not a clean fit |
| Q3_K_M at 8,192 tokens | 13.4865 | 0.5369 | 15.0234 to 16.0234 | Not a fit |
| Q4_K_M | 16.5186 | 0.2684 | 17.7870 to 18.7870 | Does not fit |
Which quant is best is therefore a trade you make yourself: Q2_K leaves room for context (at 32,768 tokens, 10.6626 + 2.1475 + 1 to 2 = 13.8101 to 14.8101 GB), Q3_K_M leaves almost none. A 14B at Q6_K is the safer 16 GB choice if you want headroom. More on this family: qwen-vram-requirements. Any layers that spill to system RAM run slower; speed is unknown here, see moe-cpu-offload.
Can 16GB VRAM run a 32B model?
Not at Q4_K_M, and only borderline at Q2_K. Qwen2.5-32B at Q4_K_M is 32.5e9 x 4.8944 / 8 / 1e9 = 19.8835 GB of weights, 21.9572 to 22.9572 GB in total (table above). At Q3_K_M the weights alone are 32.5e9 x 3.9960 / 8 / 1e9 = 16.2338 GB, over 16. At Q2_K they are 12.8347 GB, giving 12.8347 + 1.0737 + 1 to 2 = 14.9084 to 15.9084 GB at 4,096 tokens: not a clean fit. The full page is vram-for-32b-model.
Is 16GB enough for a 70B model?
No. The README’s Memory/Disk table lists Llama 3.1 70B at 43.1 GB for Q4_K_M (original 280.9 GB), 2.69 times 16 GB. To fit 70e9 parameters of weights in 16 GB you would need 16e9 x 8 / 70e9 = 1.8286 bits per weight, below the 3.1593 of Q2_K and below the README’s lowest listed 2.0042 (IQ1_S) (70e9 x 3.1593 / 8 / 1e9 = 27.6439 GB), before any KV cache. Offload to system RAM or a second card is the way in: vram-for-70b-model, multi-gpu-llm.
Which GPUs have 16GB of VRAM?
From vendor pages read on 2026-10-13. NVIDIA pages do not list memory bandwidth; AMD pages list “Up to” figures. Prices: check current retailer price.
| Card | Memory | Interface | Power per vendor page |
|---|---|---|---|
| RTX 5080 | 16 GB GDDR7 | 256-bit | 360 W total graphics power |
| RTX 5070 Ti | 16 GB GDDR7 | 256-bit | 300 W |
| RTX 5060 Ti | 16 GB / 8 GB GDDR7 | 128-bit | 180 W |
| RTX 4080 / 4080 SUPER | 16 GB GDDR6X | 256-bit | 320 W |
| RTX 4070 Ti SUPER | 16 GB GDDR6X | 256-bit | 285 W |
| RTX 4060 Ti | 16 GB GDDR6 or 8 GB GDDR6 | 128-bit | 165 or 160 W |
| RX 9070 XT | Max 16 GB GDDR6 | 256-bit, up to 640 GB/s | 304W typical board power |
| RX 9060 XT | Max 16 GB GDDR6 | 128-bit, up to 320 GB/s | 160W typical board power |
| RX 7800 XT | Max 16 GB GDDR6 | 256-bit, up to 624 GB/s | 263 W typical board power |
| RTX A4000 | 16GB GDDR6 with ECC | PCIe Gen 4 x16 (memory interface: unknown) | 140 W max, single slot |
Capacity decides what fits, so every 16 GB row above fits the same models. The 5060 Ti and 4060 Ti are also sold with 8 GB, so confirm the size (see how-to-check-vram). Speed differences between the cards: unknown. Deeper card pages: rtx-5060-ti-16gb-llm, gpu-vram-list, amd-gpu-for-local-llm.
Is the RTX 5070 Ti good for local LLMs?
It is a 16 GB card, so it fits the models in the tables above and nothing larger. NVIDIA’s RTX 5070 family page lists the 5070 Ti with “16 GB GDDR7”, a “256-bit” memory interface and 300 W total graphics power, against 12 GB and 192-bit for the RTX 5070. Memory bandwidth is not listed there, so it is unknown here. The extra 4 GB over the 12 GB RTX 5070 is what moves a 14B from Q4_K_M only to Q6_K (12gb-vram-llm).
Is the RTX 5080 good for local LLMs?
It has the same 16 GB, so for fit it is no better than the 5070 Ti. NVIDIA’s RTX 5080 page lists “16 GB GDDR7”, “256-bit” and 360 W total graphics power for the Founders Edition design, with 3x PCIe 8-pin cables or a 450 W or greater PCIe Gen 5 cable. Whether it is faster than the 5070 Ti for a given model is unknown here. If a model does not fit in 16 GB, the next capacity tier is 24gb-vram-llm.
Questions
Is 16GB VRAM enough for local LLM?
Yes for models up to about 14B parameters. Qwen2.5-14B at Q6_K and 4,096 tokens totals 13.8654 to 14.8654 GB including an assumed 1 to 2 GB overhead. It is not enough for a 32B at Q4_K_M (21.9572 to 22.9572 GB) or a 70B (43.1 GB of weights at Q4_K_M per the llama.cpp README).
What LLM can I run with 16GB VRAM?
A 7B to 14B model at Q4_K_M to Q6_K, or Qwen3.8-27B at Q2_K on paper. Qwen2.5-14B Q4_K_M is 10.7988 to 11.7988 GB at 4,096 tokens and 13.2147 to 14.2147 GB at 16,384 tokens.
What’s the best setup for Qwen3.8 27B for a 16 gig VRAM?
Q2_K is the only quant with clear margin: 11.9310 to 12.9310 GB at 4,096 tokens under the assumption that only 16 attention layers keep a KV cache. Q3_K_M totals 14.7549 to 15.7549 GB and is borderline. Q4_K_M weights alone are 16.5186 GB. The memory for linear-attention state is unknown.
Which of the 16GB VRAM Qwen3.8 27B quants is the best?
No source read ranks them for quality, so that is unknown. By fit alone, Q3_K_M is the largest quant that can fit and it is borderline; Q2_K leaves room for context. Q4_K_M and larger do not fit.
Can 16GB VRAM run a 32B model?
Not at Q4_K_M: Qwen2.5-32B needs 19.8835 GB for weights alone. At Q2_K the total is 14.9084 to 15.9084 GB at 4,096 tokens, which is borderline, and Q3_K_M weights (16.2338 GB) exceed 16 GB.
Is 16GB enough for a 70B model?
No. Llama 3.1 70B at Q4_K_M is 43.1 GB per the llama.cpp README, and fitting 70e9 weights in 16 GB would need 1.8286 bits per weight, below the README’s lowest listed bits per weight (IQ1_S 2.0042; Q2_K is 3.1593).
Sources read on 2026-10-13
- NVIDIA RTX 5080: 16 GB GDDR7, 256-bit, 360 W, power connectors; bandwidth not listed.
- NVIDIA RTX 5070 family: 5070 Ti 16 GB GDDR7, 256-bit, 300 W; 5070 12 GB, 192-bit, 250 W.
- NVIDIA RTX 5060 family: 5060 Ti “16 GB / 8 GB GDDR7”, 128-bit, 180 W.
- NVIDIA RTX 4080 family, RTX 4070 family, RTX 4060 family: memory, interface and power rows in the card table.
- NVIDIA RTX A4000: 16GB GDDR6 with ECC, PCIe Gen 4 x16, 140 W, single slot.
- AMD RX 9070 XT, RX 9060 XT, RX 7800 XT: max memory, type, interface, bandwidth, board power (WebFetch returned HTTP 503; page bodies fetched with curl).
- Qwen3.8-27B card and
config.json: 27B parameters, 64 layers, 16 full-attention layers, 4 KV heads, head dimension 256, 262,144 context, bfloat16. - Qwen2.5-14B and Qwen2.5-32B cards and
config.json: 14.7B and 32.5B parameters, 48 and 64 layers, 8 KV heads, hidden size 5120, 40 query heads, bfloat16. - llama.cpp quantize README: bits per weight by quant; Llama 3.1 70B 43.1 GB Q4_K_M, 280.9 GB original.
Related
- 12gb-vram-llm and 24gb-vram-llm are the tiers either side.
- 24gb-vs-16gb-vs-12gb-vram compares the three.
- rtx-5060-ti-16gb-llm covers the 5060 Ti and RX 9060 XT.
- qwen-vram-requirements, vram-for-32b-model and vram-for-70b-model go deep by model.
- vram-requirements and methodology are the formulas.
- gpu-vram-list, amd-gpu-for-local-llm, moe-cpu-offload and multi-gpu-llm cover cards and offload.