Best LLMs for 48 GB VRAM (2026)
48 GB: The 70B Gateway
48 GB is the threshold where the 70B class first becomes usable, though not at full Q4_K_M quality: Llama 3.3 70B's Q4_K_M weights alone are 42.6 GB, and once KV cache and runtime overhead are added the real total is closer to 49 GB, just past what a real 48 GB pool has to give once PCIe pooling losses or (on Apple Silicon) the OS memory reservation are accounted for. The realistic quantization at this tier is Q3_K_M (~39 GB total), a genuinely usable but noticeably compressed 70B. Three paths get you there: dual consumer GPUs (2x RTX 3090 = 48 GB, ~$1500-1800 used), single workstation GPU (RTX A6000 48GB, ~$3000-4000 used), or Apple Silicon (M4 Pro 48GB, $2399, tighter still at Q2_K, see below). Dual 3090s are most popular: lowest cost, and llama.cpp tensor parallelism makes multi-GPU straightforward. Key: multi-GPU scaling is sublinear: ~90% usable pooled VRAM, roughly 1.5x speed over a single card (not 2x). NVLink not needed for inference. If you want genuine Q4_K_M quality on a 70B, see the 64-72 GB tier instead (a third pooled GPU, or an M4 Max).
70B at Q3: The Dense Option
Llama 3.3 70B is still the defining dense model for this tier in mid-2026: no newer dense 70-72B model has shipped from any major lab, since the frontier moved to MoE architectures above ~35B (see the alternative paths below). Its Q4_K_M weights alone are ~42.6 GB, but the real total (weights + KV cache + runtime overhead) comes to about 49 GB, more than a real 48 GB pool has once pooling losses are counted; the quantization that actually fits at 48 GB is Q3_K_M, about 39 GB total. On dual RTX 3090s that's roughly 15-16 tok/s at 4K context. Llama 3.3 matches Llama 3.1 405B on many benchmarks at one-fifth the size, so even at Q3_K_M it remains a strong model. Qwen2.5 72B (~43.9 GB at Q4_K_M) and DeepSeek R1 Distill Llama 70B (~42.6 GB at Q4_K_M) are the same story: strong alternatives, same 48 GB ceiling, same real-world Q3_K_M quantization. DeepSeek R1 Distill also adds chain-of-thought, generating 2-5x more tokens per query.
The Alternative: A 24 GB-Tier Model at Near-Lossless Quality
70B isn't the only good use of 48 GB, and a growing share of the 2026 community explicitly prefers the alternative: take the 24 GB tier's best model, Qwen 3.6 27B, and run it at Q8_0 instead of Q4_K_M. The weights grow from ~16.4 GB to ~28.7 GB, but on 48 GB that barely matters: the total comes to about 42 GB even at a generous 128K tokens of context, near-lossless quality, and none of the multi-GPU tensor-split complexity or sublinear scaling that comes with actually running 70B across two cards. Push all the way to its full 262,144-token native context and the total climbs to about 51 GB, just past a real 48 GB card's usable capacity: comfortable up to roughly 128K, tight beyond that. The trade-off versus a genuine 70B is real: Qwen 3.6 27B is still a 27B-class model underneath the higher quantization, so its ceiling on tasks that need very broad world knowledge is lower than 70B's, even though its coding and reasoning benchmarks are competitive. If your workload is coding, agentic tasks, or anything where Qwen 3.6 27B already scores well, this is arguably the better use of 48 GB. If you specifically need 70B's broader knowledge base, the daily-driver path above is still the right call.
| Model | Q8_0 |
|---|---|
| Qwen 3.6 27B | 41.8 GB |
The Third Path: Big MoE Models via CPU Offload
Neither Meta nor Alibaba has shipped a new dense 70-72B model since Llama 3.3 and Qwen 2.5 72B: every frontier lab moved to Mixture of Experts above roughly 35B, which is why the 'daily driver' section above still points at 2024-era dense models. 2026's actual answer for more capability at 48 GB is MoE-via-offload, the same technique the 12 GB tier uses, scaled up. Nemotron 3 Super (120B total, 12B active, mmluPro 83.7) needs about 57.7 GB at its recommended Q3_K_M, over 48 GB of pure VRAM, but with the attention layers and shared parameters held on the GPU and the routed expert FFNs offloaded to system RAM (64-128 GB, which this tier's builds already call for), it runs at usable speeds well above what a 12B active-parameter model's raw compute would suggest, since only a fraction of its 120B total ever computes per token. GLM-4.5 Air (106B total, 12B active) is the tighter fit of the two at roughly 51 GB and offloads even less. Neither is as simple to set up as a model that fits outright, and both are slower than the dense-70B or Qwen-3.6-27B-at-Q8 paths above, but for the specific case of wanting more raw capability than 70B-dense offers without a third GPU, this is the 2026 route to it. Use the --n-cpu-moe flag in llama.cpp to control the offload split.
2x RTX 3090 in Practice
Requirements: motherboard with 2 PCIe x16 slots, 1000W+ PSU (dual 3090s draw ~700W), adequate airflow. llama.cpp flags: --tensor-split and --split-mode row (~2 tok/s extra). Ollama auto-detects multi-GPU. LM Studio does NOT support multi-GPU. Set GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 to allow system RAM fallback instead of OOM crash.
Single-GPU Options: A6000 and Mac
RTX A6000 48GB (used, $3000-4000): ECC, 768 GB/s, blower cooler, no multi-GPU complexity, runs Llama 3.3 70B at Q3_K_M around 14 tok/s. M4 Pro 48GB (Mac mini, $2399): the OS's unified-memory reservation leaves less headroom than a discrete card's 48 GB, so 70B lands at Q2_K, roughly 7 tok/s, at ~50W, near-silent. Unified memory eliminates multi-GPU complexity entirely. For simplicity and noise over raw speed, Mac is compelling, just at a real quality cost versus the A6000. The bandwidth gap is the bigger reason the A6000 outpaces the Mac on tokens/sec despite both holding roughly the same 48 GB:
KV Cache and Long Context
Llama 3.3 70B's KV cache is smaller than a naive per-token estimate suggests, because it uses grouped-query attention (8 KV heads, not one per attention head): at FP16, the cache is about 2.7 GB at 8K context, 10.7 GB at 32K, and around 43 GB at its full 128K context. That last figure is the one that actually matters for this tier: on top of Q3_K_M's ~34 GB of weights, a full 128K-context KV cache alone would exceed 48 GB, so pushing to long context on a 70B at this tier still needs KV cache quantization. llama.cpp flags: --cache-type-k q8_0 --cache-type-v q8_0 (50-75% reduction). TurboQuant (3.5 bpw) reduces further. For comfortable 128K context on a 70B, 64 GB+ is the realistic minimum.
Beyond 70B: What 48 GB Cannot Do
Llama 3.1 405B Q4 needs ~279 GB total. Llama 4 Scout (109B MoE) Q4 needs ~76 GB total, well over. Mixtral 8x22B Q4 needs ~97 GB total. Qwen3 235B-A22B Q4 needs ~143 GB of weights alone. The 70-72B dense class is the ceiling, and even that needs a third GPU to sit fully in VRAM rather than partially offload. Good news: 70B models have improved so much (Llama 3.3 matching 405B) that you are not missing much.
Frequently asked questions
- One A6000 or two RTX 3090s?
- Dual 3090s (~$1500-1800) give same 48 GB at roughly half the cost of used A6000 ($3000-4000). A6000 wins on simplicity, power (300W vs 700W), ECC. For hobbyists, dual 3090s are better value.
- Can I mix different GPU models?
- Technically yes via llama.cpp tensor split, but effective speed limited by slowest card. Use larger-VRAM card as --main-gpu.
- System RAM needed with 48 GB VRAM?
- At least 64 GB, preferably 128 GB. System RAM used for model loading and overflow. 32 GB is absolute minimum: you will hit swap during loading.
- Apple Silicon vs dual 3090s for 48 GB?
- Dual 3090s: Q3_K_M on 70B, around 15-16 tok/s. M4 Pro: the unified-memory OS reservation leaves less usable headroom, so it lands at Q2_K, around 7 tok/s, but at ~50W vs 700W and near-silent. Speed and quality vs simplicity trade-off.
- Should I run 70B, or a smaller model at higher quality?
- Both are legitimate uses of 48 GB. 70B gives you the broadest world knowledge but needs multi-GPU tensor-split and only fits at Q3_K_M (Q2_K on Apple Silicon), a real quality cost versus Q4_K_M or higher. Qwen 3.6 27B at Q8_0 gives near-lossless quality of a smaller model on a single card with room for 128K+ tokens of context, and for coding/agentic work its benchmarks are already competitive with 70B. If you're unsure, start with the smaller model at Q8: it's simpler to set up, and you can always add a third GPU for full Q4_K_M-quality 70B later.
- What about Nemotron 3 Super or GLM-4.5 Air instead of a 70B?
- Both are big MoE models (120B/12B active and 106B/12B active respectively) that don't fit purely in 48 GB VRAM (Nemotron 3 Super needs ~58 GB and GLM-4.5 Air ~51 GB at their recommended quants), but with CPU offload of the routed experts (--n-cpu-moe in llama.cpp) and the 64-128 GB of system RAM this tier already calls for, both run at usable speeds. It's a more involved setup than a model that fits outright, so it's worth trying only if you specifically want more capability than 70B-dense or Qwen 3.6 27B at Q8 already gives you.