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Hardware October 28, 2026

Is 24GB of VRAM Enough in 2026? Yes, Up to 35B

Yes, 24GB is enough for the models most people run locally in 2026. Qwen3.8 27B and Qwen3.6 35B-A3B both fit at 4 bits with about 100K tokens of context. 24GB is not enough for the same models at 8 bits, for a dense 70B, or for gpt-oss 120B without CPU offload. The surprise is the price of the next step: in October 2026, 32GB costs about $300 more than a used RTX 3090 on AMD, and about $5,400 more on NVIDIA.

Bottom Line

  • Yes, 24GB is enough for the models most people run in 2026. Qwen3.8 27B and Qwen3.6 35B-A3B both fit at 4 bits with about 100K tokens of context.
  • The best model on 24GB is Qwen3.8 27B at 4 bits (16.35 GiB). The full pick list is on best local LLM for 24GB VRAM.
  • 24GB is not enough for: 8-bit 27B (27.05 GiB), 8-bit 35B-A3B (34.37 GiB), gpt-oss 120B (59.03 GiB), or a dense 70B at a usable quant.
  • 32GB adds quality, not a new model class. It runs the same 27B at Q8_0 with about 56K of context.
  • The price of the step depends on the brand. In October 2026, a used RTX 3090 averaged $1,541. A 32GB R9700 had listings from $1,850. A 32GB RTX 5090 had listings from $6,987.

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

What Fits in 24GB, With Context

A 24GB card holds 24 GiB. On a headless card, we budget about 22.6 GiB for weights plus KV cache: 0.4 GiB for the driver and about 1 GiB for llama.cpp compute buffers. These are our assumptions, and the 24GB model page shows a third-party measurement that agrees. A monitor on the same card takes more.

File sizes are byte counts from the Hugging Face API, read 2026-10-28. KV cache per token comes from each config.json: Qwen3.8 27B costs 64 KiB per token at f16, Qwen3.6 35B-A3B 20 KiB, gpt-oss 20B 24 KiB. The two Qwen models also keep a fixed-size state for their linear-attention layers: 0.14 GiB for the 27B and 0.06 GiB for the 35B-A3B. Context = (22.6 GiB − file − fixed state) ÷ cache per token. This is our arithmetic, and it matches the 24GB model page.

ModelFileSize (GiB)Context on 24GB (f16 cache)Verdict
gpt-oss 20B (MoE)MXFP4 (ggml-org)11.28Full 128K, with GiB to spareFits easily
Qwen3.8 27B (dense)UD-Q4_K_XL (Unsloth)16.35About 100KFits — the best pick
Qwen3.8 27BUD-Q5_K_M (Unsloth)18.41About 66KFits
Qwen3.8 27BUD-Q6_K (Unsloth)20.47About 32KFits, short context
Qwen3.8 27BQ8_027.05—Does not fit
Qwen3.6 35B-A3B (MoE)UD-Q4_K_M (Unsloth)20.61About 101KFits
Qwen3.6 35B-A3BUD-Q5_K_XL (Unsloth)24.77—Does not fit
Qwen3.6 35B-A3BQ8_034.37—Does not fit
gpt-oss 120B (MoE)MXFP4 (ggml-org)59.03—Only with experts in system RAM
Dense 70BQ4_K_Mabout 40—No — see can 24GB run a 70B

Double the context with a Q8 cache. A q8_0 KV cache halves the cost per token. That takes Qwen3.8 27B UD-Q4_K_XL from about 100K to its full 128K and beyond. See KV cache quantization.

gpt-oss 120B is the one model people try to force. llama.cpp can keep the expert weights in system RAM with --n-cpu-moe. It runs, but your system RAM and PCIe set the speed, not the card. The flags are in llama.cpp MoE offload flags explained.

What 32GB Adds (and What It Does Not)

A 32GB card gives about 30.6 GiB for weights plus cache by the same budget.

ModelBest file on 24GBBest file on 32GBWhat changes
Qwen3.8 27BUD-Q4_K_XL, 16.35 GiB, ~100K ctxQ8_0, 27.05 GiB, ~56K ctx8-bit weights; less context unless you use a Q8 cache (~105K)
Qwen3.6 35B-A3BUD-Q4_K_M, 20.61 GiB, ~101K ctxUD-Q6_K, 27.30 GiB, ~170K ctx6-bit weights and more context
gpt-oss 120BOffload onlyOffload onlyNothing
Dense 70BNoNoNothing

So 32GB is a quality step on the same models. It does not open a new model class. The next class (dense 70B, or gpt-oss 120B on the card) starts at 48GB to 64GB. See is 32GB of VRAM enough and is 48GB of VRAM enough.

What Each Route Costs (October 2026)

US street prices from our price reference, read by the sources below in October 2026. Prices move weekly in the 2026 memory shortage. Check current listings before you buy.

RouteVRAMPrice, October 2026BandwidthSoftware
Used RTX 309024GB$1,541 average, median $1,549, 317 listings (resaleprices.com, Oct 22)936 GB/sCUDA
Used RTX 409024GB$3,000 median (resaleprices.com, Oct 22)Faster than a 3090CUDA
AMD Radeon AI PRO R970032GB8 listings from $1,850; October daily lowest average $1,683-2,016 (GPU Poet)640 GB/s (AMD spec)ROCm
RTX 509032GB92 listings from $6,987 (GPU Poet)Far ahead of bothCUDA

The thing most “is 24GB enough” answers miss: the price of 32GB now depends on the brand. On AMD, 32GB costs about $300 more than a used 3090 ($1,850 vs $1,541). On NVIDIA, it costs about $5,400 more ($6,987 vs $1,541). Older advice treats 32GB as a 5090-only, money-no-object tier. In October 2026 it is not.

The R9700 trade-off. It has 33% more memory than a 3090 and about 32% less bandwidth (640 vs 936 GB/s). So on a model that fits both cards, the 3090 generates faster. The R9700 wins only when you need the 8-bit file. The full comparison is in R9700 vs RTX 3090.

EVGA GeForce RTX 3090 24GB — the 24GB value pick. Buy used with care: see how to buy a used RTX 3090 safely.

XFX Radeon AI PRO R9700 32GB — the cheapest step to 32GB, if you run 8-bit files and accept ROCm.

GIGABYTE RTX 5090 WINDFORCE 32GB — 32GB on CUDA, at about 4.5x the price of a used 3090.

The Decision

Your situationAnswer
Coding agent or chat on 27B / 35B models24GB is enough. Run 4-bit with a Q8 cache.
You already own a 3090 or 4090Keep it. 32GB adds 8-bit quality, not a new model.
You want 8-bit 27B or 6-bit 35B-A3B32GB. The R9700 is the cheapest route.
You want gpt-oss 120B or a dense 70B on the GPU24GB and 32GB both fail. Go to 48GB or more.
Budget near $1,500A used 3090.
You need CUDA and 32GBRTX 5090, at a large premium.

The method, so you can check your own case: take the file size from the model’s Hugging Face page. Add about 1.4 GiB for the driver and buffers. Then add the KV cache: per-token cost × context length. If the total is under 24 GiB, it fits. The numbers above do not transfer exactly between cards or drivers. Watch nvidia-smi (or rocm-smi) during your first load.

See Also

Sources

Read next

Is 32GB of VRAM Enough for Local AI in 2026?
32GB comfortably runs the 27B agentic tier and cannot run a dense 70B — that part is settled. The unsettled part is context: at 32GB you get roughly 52K tokens on a dense 32B before the KV cache runs you out. The exact budget, and what 32GB costs by route.
Is 48GB of VRAM Enough for Local AI in 2026?
48GB is the tier that finally runs a dense 70B — with about 19K tokens of context left over, not 128K. A 70B's full 128K KV cache is exactly 40 GiB at FP16, the same size as its weights. The arithmetic, the four routes, and what 48GB costs in 2026.
Is 96GB of VRAM Enough for Local AI in 2026?
96GB is the first tier where a dense 70B runs at its full 128K window: 42.5GB of Q4 weights plus exactly 40 GiB of FP16 KV cache is 82.5GB, and it fits. What 96GB unlocks, what it still cannot hold, and what the one card that has it costs in 2026.
Best Local LLM for 24GB VRAM (2026): Qwen3.8 27B Wins
Best local LLM for 24GB VRAM: Qwen3.8 27B at 4 bits (16.35 GiB) with about 96K of context. Byte-exact GGUF sizes, KV-cache math for gpt-oss 20B and Qwen3.6 35B-A3B, and October 2026 card prices.