Best LLM for 64GB VRAM GPUs (2026): Laguna S 2.1 Wins

Bottom Line (2026)

• No consumer GPU has 64GB of VRAM. The RTX 5090, Radeon AI PRO R9700 and Arc Pro B70 stop at 32GB. You get 64GB from two 32GB cards, or from one workstation or datacenter card: a used AMD Instinct MI210 (64GB), an RTX PRO 5000 Blackwell 72GB, or an RTX PRO 6000 96GB. Our 64GB VRAM setup guide prices every route.
• Two 32GB cards hold just under 64 GiB. Each RTX 5090 carries 32 GiB of GDDR7, and each card loses a few hundred MiB to its CUDA context. Guides that use “59.6 GiB” convert 64 decimal GB and undercount.
• Best overall pick: Laguna S 2.1 at Unsloth UD-IQ4_XS — 57.57 GB (53.61 GiB), 118B/8B MoE, 1M context, 70.2% on Terminal-Bench 2.1 per its card.
• Best single-card pick: Qwen3.8-27B at Q8_0 — 27.05 GiB, fits one RTX 5090 and leaves the second card free. Its card reports 73.0 on Terminal-Bench 2.1 with a different harness, so do not compare the two scores directly.
• Best fast agent model: Laguna XS 2.1 at Q4_K_M (18.88 GiB) — 3B active params on one card, large context headroom.
• gpt-oss 120B is borderline on two 5090s. The MXFP4 file is 59.03 GiB, against just under 64 GiB across both cards. It loads only at a short context. If it fails, move one or two expert layers to the CPU.
• New 3-bit option: Ornith 1.5 122B at UD-Q3_K_XL is 55.66 GiB and fits both cards at about 64K context. On one 72GB RTX PRO 5000, the 4-bit IQ4_XS (65.39 GiB) fits with no split.
• Do not buy for this: any model above ~120B total. The August 2026 MoEs (Qwen3.8-Flash-Next, DeepSeek-V4-Flash) do not fit even at 1-bit.

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

VRAM Is Not RAM

Most “64GB local LLM” guides answer the Mac question. If you searched for VRAM, they answer the wrong one.

64GB unified RAM (Mac mini M5 Pro, Mac Studio M5 Max). One shared CPU/GPU pool at 307-614 GB/s. Every byte is usable by the model. Setup is trivial: pull the model, run it. Generation is slow but steady.

64GB VRAM (multi-GPU or workstation card). Almost always two cards. Two RTX 5090s at 32GB each run at 1792 GB/s per card — about 3x an M5 Max and almost 6x an M5 Pro. Generation is dramatically faster. The catch is that the memory is not one pool.

The practical rule: a model must fit inside one card’s VRAM unless you split it. Splitting works. llama.cpp distributes layers across GPUs automatically, and vLLM runs tensor parallelism with --tensor-parallel-size 2. Both cost throughput, because activations cross the PCIe bus on every token. A model that fits one card runs faster than the same model split across two.

So at 64GB VRAM you actually have two strategies:

1. Split a big model. Run one 50-60GB model across both cards. Highest quality, some PCIe tax.
2. Two independent models. Load a 20-30GB coder on GPU 0 and a tool-calling model on GPU 1. No PCIe tax, and OpenClaw can route between them.

Strategy 2 is underrated. Most people buy the second card and never use it that way.

Top Picks for 64GB VRAM

1. Laguna S 2.1 (118B/8B MoE) at UD-IQ4_XS — the flagship pick

Poolside shipped Laguna S 2.1 on July 21, 2026 under OpenMDW-1.1. It is 118B total parameters with 8B active per token, a 1M token context window, and 70.2 on Terminal-Bench 2.1 — beating models ten times its size.

The Unsloth quant sizes decide everything here:

• UD-Q2_K_XL — 39.68 GB / 36.96 GiB (fits one 48GB card)
• UD-IQ4_NL — 58.75 GB / 54.71 GiB (also fits 64GB)
• UD-IQ4_XS — 57.57 GB / 53.61 GiB (the 64GB VRAM pick)
• UD-Q4_K_M — 73.12 GB / 68.10 GiB (does not fit 64GB)

Sizes are byte-exact from the Hugging Face file list, read 2026-09-30. At 53.61 GiB you have about 10 GiB left for KV cache and CUDA overhead, so cap context well below 1M. llama.cpp splits the layers across both cards by default. With 8B active parameters per token, generation stays fast despite the 118B total.

# llama.cpp across two 5090s (layer split is the default)
llama-server -hf unsloth/Laguna-S-2.1-GGUF:UD-IQ4_XS -ngl 99 -c 16384

2. Qwen3.8-27B at Q8_0 — best single-card pick

A dense 27B, released August 2026 under Apache-2.0 with a 262K native context. The Unsloth Q8_0 file is 29.05 GB (27.05 GiB). It fits one RTX 5090 with about 4 GiB to spare before CUDA overhead, so keep context modest or drop to a 6-bit build. The second card stays free for another model. Its card reports 73.0 on Terminal-Bench 2.1 (Terminus harness) and 61.7 on SWE-bench Pro.

If you split it across both cards, the BF16 weights (50.90 GiB) also fit, at full precision.

Also on one card: Laguna XS 2.1 (33B/3B MoE)

Poolside’s July 2, 2026 release. 33B total, 3B active, 256K native context, OpenMDW-1.1. Official GGUFs live at huggingface.co/poolside/Laguna-XS-2.1-GGUF and it is in the Ollama library.

Q4_K_M is 20.27 GB (18.88 GiB). The official repo has only Q4_K_M and BF16 (66.93 GB), so there is no official 8-bit build. Its card reports 70.9% on SWE-bench Verified, against 69.9% for its predecessor XS.2.

ollama pull laguna-xs-2.1
openclaw config set agents.defaults.models.agent ollama/laguna-xs-2.1

This is the model to run when you want the second GPU free for something else.

3. gpt-oss 120B — borderline on two 32GB cards

The ggml-org MXFP4 file is 59.03 GiB, and every Unsloth 4-bit build is 58.3-60.9 GiB. Two RTX 5090s carry just under 64 GiB, and each card loses a few hundred MiB to its CUDA context. The llama.cpp guide (discussion #15396) gives a total of “64.0 GB” at 8,192 tokens, 64.9 at 32K and 68.5 at 131K. The guide does not say if “GB” means 10^9 bytes (59.6 GiB) or GiB. Either way, 8K context is at the edge, and 131K does not fit.

So test it at a short context first. If the load fails, move one or two expert layers to system RAM with --n-cpu-moe. That still works well for an MoE: the same guide reports about 30 tok/s on one RTX 5090 with 21 expert layers on the CPU. We found no measured figure for two 5090s. Its tool-call JSON is still the cleanest we have found for OpenClaw loops, so it is worth the offload if tool calling matters most.

ollama pull gpt-oss:120b
openclaw config set agents.defaults.models.agent ollama/gpt-oss:120b
openclaw config set agents.defaults.keep_alive 30m

4. Laguna XS 2.1 (Q4_K_M) — best speed-per-watt

At 18.88 GiB this fits one RTX 5090 with about 11 GiB spare for KV cache. That buys roughly 64K context on a single card at full 1792 GB/s bandwidth, no PCIe tax. Pair it with Qwen3.8-27B on the second card and you have a two-model OpenClaw setup that never swaps.

5. Dual-model split — the setup most people miss

Put Laguna XS 2.1 Q4 on GPU 0 (20GB) and a tool-calling model on GPU 1. Set CUDA_VISIBLE_DEVICES per Ollama instance so each model pins to one card. Both run at full single-card bandwidth. OpenClaw routes chat to one and agent loops to the other.

What Fits in 64GB VRAM

6. Speed: what the bandwidth actually buys

An RTX 5090 moves 1792 GB/s. A 64GB Mac mini M5 Pro moves 307 GB/s, and an M5 Max Mac Studio 614 GB/s. Generation speed on a memory-bound model tracks bandwidth almost linearly, so the same model runs several times faster on the GPU rig.

Measured numbers for this tier are thin. One community run put Laguna S 2.1 at Q4_K_M on a single RTX 5090, with the experts that did not fit on the CPU, at about 19 tok/s generation (llama.cpp, --fit). With all experts on the CPU it fell to 8.2 tok/s. We found no measured figure for a 100-120B MoE fully on two 5090s.

Two things eat into that. Splitting a model across cards adds a PCIe round trip per token. And MoE models like Laguna XS 2.1 activate only 3B parameters per token, so they were already fast on slower memory — the GPU gain is smaller there than on a dense 70B.

Buy 64GB of VRAM for dense models, long agent runs, and batch throughput. Buy unified memory for simplicity and a model list that fits one pool.

What Does NOT Fit in 64GB VRAM

These come up constantly in searches. None of them run on two consumer cards.

• Kimi K3 (open weights July 27, 2026) — 2.8T total / 104B active, about 1.56TB of files, under the “Kimi K3 License”. Datacenter only.
• GLM-5.2 (~750B, MIT) — needs about 256GB minimum at 2-bit. The Unsloth 4-bit builds run 365-467GB.
• Kimi K2.7-Code (1T total / 32B active) — still around 340GB at 2-bit.
• gpt-oss 120B at long context (59.03 GiB of weights) — it loads only at a short context on two 32GB cards. At 131K it needs CPU expert offload.
• Laguna S 2.1 at UD-Q4_K_M (68.10 GiB) — drop to UD-IQ4_XS at 53.61 GiB instead.
• The August 2026 MoEs. Qwen3.8-Flash-Next’s smallest file (UD-IQ1_S) is 67.56 GiB, and DeepSeek-V4-Flash-0731’s smallest is 76.87 GiB. Neither fits even at 1-bit.

The ceiling at 64GB VRAM in November 2026 is still the 100-120B MoE class at 4-bit. Everything above that is a Mac-cluster or rented-GPU problem.

How to Actually Get 64GB VRAM

2x RTX 5090 (32GB each) = 64GB. The obvious build, and now the expensive one. GPU Poet’s October 2026 daily lowest average ran $5,986-6,932 per card, so a pair costs about $12,000-13,900 (as of November 2026). The $4,299 September deal is gone. Highest bandwidth per card at 1792 GB/s. Two cards pull serious power, so plan a 1200W-plus PSU and check that your board gives both slots at least PCIe x8. Physical clearance is real: most 5090s are three slots wide.

2x RTX A6000 (48GB each) = 96GB, 64GB used. Blower-style workstation cards fit side by side and run far cooler under sustained load. Lower bandwidth than a 5090, but 48GB per card means Qwen3.8-27B at Q8_0 with long context, or Laguna S 2.1 at UD-Q2_K_XL, fit inside a single card with no tensor splitting at all. That single-card fit often beats raw bandwidth.

1x RTX PRO 6000 Blackwell (96GB). One card, one pool, no tensor parallelism, no PCIe tax, one power connector budget. Anything at 64GB fits with room to spare, and the 68.10 GiB Laguna S 2.1 UD-Q4_K_M build that misses 64GB fits here, and so does gpt-oss 120B with its full context. If your workload is one big model rather than many small ones, this is the cleaner answer.

2x Radeon AI PRO R9700 (32GB each) = 64GB. The cheaper pair. GPU Poet’s October 2026 daily lowest average ran $1,683-2,727 per card (as of November 2026). It uses ROCm, not CUDA, so check that your runtime supports it.

1x used AMD Instinct MI210 (64GB HBM2e). The only single card with exactly 64GB. eBay asking prices started near $4,299 in mid-October 2026. It is a passive datacenter card: it needs server airflow.

1x RTX PRO 5000 Blackwell 72GB. One card that holds Ornith 1.5 122B at 4-bit (65.39 GiB) with no split. Stock is thin, so check current listings.

Do not mix generations casually. A 5090 paired with a 3090 runs split inference at closer to the 3090’s speed on the layers it holds. Matching cards keeps the math predictable.

Common Mistakes

1. Reading a 64GB unified-RAM guide and buying GPUs. The model lists differ. On a Mac the whole 64GB is one pool; on two GPUs a 53.6 GiB model must be split and a 34GB model may not fit one card.
2. Assuming 64GB total means a 60GB model runs well. Two 32GB cards hold just under 64 GiB, minus a CUDA context per card and the KV cache. gpt-oss 120B at 59.03 GiB loads only at a short context; offload one or two expert layers with --n-cpu-moe or you will OOM at load.
3. Pulling Laguna S 2.1 at UD-Q4_K_M because “it’s the standard quant”. It is 68.10 GiB. Use UD-IQ4_XS at 53.61 GiB.
4. Ignoring the PCIe tax. Splitting a model across two cards moves activations over the bus every token. If a model fits one card, keep it on one card.
5. Leaving the second GPU idle. Pin one model per card with CUDA_VISIBLE_DEVICES and run chat and agent loops in parallel at full bandwidth.
6. Undersizing the PSU. Two 5090s plus a CPU under sustained inference load is not a 850W build.

Before you order parts, check the tested hardware list for current prices by tier.

Sources

• Hugging Face file trees, summed by bytes on 2026-11-03: unsloth/Laguna-S-2.1-GGUF (UD-IQ4_XS 57,566,704,320 bytes), unsloth/Ornith-1.5-122B-GGUF (UD-Q3_K_XL 59,761,254,545 bytes), bartowski/Ornith-1.5-122B-GGUF (IQ4_XS 70,214,361,600 bytes).
• llama.cpp gpt-oss guide, discussion #15396 — memory table and the RTX 5090 --n-cpu-moe 21 example, read 2026-11-03.
• GPU Poet October 2026 price pages for the RTX 5090 and Radeon AI PRO R9700, read 2026-10-30.
• Lenovo Press LP1862 — AMD Instinct MI210: 64GB HBM2e, 1.6 TB/s, 300W, passive.

See Also

• Best 64GB VRAM Setup for Local LLMs — which hardware gives you 64GB, priced route by route
• Best Local LLMs for 64GB RAM — the unified memory version of this question: one shared pool at 307-614 GB/s, not split VRAM
• Best Local LLM for RTX 5090 — the single-card 32GB tier before you buy a second
• Best Local LLMs for 128GB RAM — where the 400B-class models start fitting
• Best Local LLM by GPU (hub) — per-card picks for every NVIDIA tier
• RTX 5090 vs 4090 vs Used 3090 — which two cards to actually buy