Three major model releases in the past two weeks all share the same hardware implication: you need 48GB of VRAM to run them properly. Not 24GB with clever quantization tricks. Not 32GB with partial offloading. 48GB.
Mistral Small 4 checks in at 119B total parameters with 6B active (MoE). Nemotron 3 Super is 120B total with 12B active. MiniMax M2.5 is 230B total with 10B active. Every one of these models requires more VRAM than a single consumer GPU can provide, unless you're willing to run at Q2 quantization where quality degrades noticeably.
This isn't a coincidence. It's a shift in where the open-weight frontier sits. A year ago, the sweet spot was 70B dense models that fit on a single RTX 4090. That era is ending. The next generation of "best open models" is landing in the 100–230B MoE range, and the VRAM floor to run them well is 48GB.
Quick Summary
- Three consecutive top-tier model releases (Mistral Small 4, Nemotron 3 Super, MiniMax M2.5) all require 48GB+ VRAM for clean Q4 inference
- The cheapest path to 48GB: dual RTX 3090 at ~$1,000 used or a single used NVIDIA A6000 at ~$2,500
- Cost-per-GB favors the multi-GPU approach, but single-GPU wins on bandwidth and simplicity
Why These Models Need 48GB
All three are MoE (Mixture-of-Experts) architectures. The active parameter count is low — 6B to 12B active per forward pass. But the total weight size determines VRAM requirements, not the active count.
At Q4 quantization (roughly 4 bits per parameter):
- 119B parameters × 0.5 bytes = ~60GB baseline
- Add KV cache and runtime: ~65–70GB total
- Fits in 48GB only at aggressive quantization (Q3/Q2) — quality penalty applies
Wait, that math doesn't work. Let me clarify:
At Q4_K_M (the standard GGUF quantization):
- 119B model: GGUF file ≈ 67–70GB
- Does not fit in 48GB
This is where the "48GB is the floor" framing needs precision. What 48GB actually gives you:
What fits cleanly at 48GB:
- 70B dense models at Q4 (~40GB)
- 70B dense models at Q5 (~45GB) — just barely
- Qwen2.5 72B at Q4 (~43GB)
- DeepSeek R1 70B at Q4 (~40GB)
- Llama 3.3 70B at Q4 (~40GB)
What requires 2× 48GB (96GB total):
- Mistral Small 4 (119B) at Q4 (~67GB)
- Nemotron 3 Super (120B) at Q4 (~68GB)
- MiniMax M2.5 (230B) at Q4 (~120GB)
So the real threshold is 48GB per card, 2-card setup = 96GB, to run the new MoE flagship tier without offloading. For a single-GPU "run everything at 48GB" answer, that's only true for 70B-class models.
Still, 48GB single-GPU is a significant upgrade over 24GB because it handles the full 70B tier at Q4 and Q5, and it positions you for 2-card = 96GB for the MoE tier without needing more than two cards.
Every GPU That Gets You to 48GB
Single-GPU Options
NVIDIA RTX 6000 Ada Generation (48GB)
- New price: ~$6,000–$6,500
- Memory bandwidth: 960 GB/s
- This is the current-gen professional card with 48GB. Excellent bandwidth, full support for all inference frameworks.
- Cost-per-GB: $125–$135/GB
NVIDIA A6000 (48GB, Ampere)
- Used price: $2,000–$3,000
- Memory bandwidth: 768 GB/s
- Previous-gen professional card. Slower than the Ada version but dramatically cheaper. Strong support in inference stacks.
- Cost-per-GB: $42–$63/GB
NVIDIA A40 (48GB)
- Used price: $1,500–$2,500 (data center pulls)
- Memory bandwidth: 696 GB/s
- Data center variant — no display outputs, designed for rack servers. Excellent VRAM-per-dollar for headless inference servers.
- Cost-per-GB: $31–$52/GB
AMD Instinct MI50 (16GB, 32GB HBM2)
- Used price: $300–$600 for 32GB variant
- ROCm support varies — not recommended for most local setups
Multi-GPU Paths to 48GB Total
2× RTX 3090 (48GB total)
- Cost: ~$1,000 used
- Memory bandwidth: 2× 936 GB/s but inter-GPU communication over PCIe limits effective bandwidth for cross-layer operations
- Cost-per-GB: ~$21/GB — the best VRAM/$ option
- Runs 70B dense at Q4 cleanly, both cards feeding from system RAM + GPU
2× RTX 4090 (48GB total)
- Cost: ~$3,200 new or ~$2,800 used
- Memory bandwidth: 2× 1,008 GB/s
- Cost-per-GB: ~$58/GB
- Best performance multi-GPU path at this VRAM level
2× RTX A5000 24GB (48GB total)
- Used price: ~$800–$1,200 total
- Memory bandwidth: 2× 768 GB/s
- NVLink-capable — pairs can share VRAM across NVLink bridge, giving unified 48GB pool
- Cost-per-GB: ~$17–$25/GB — compelling for professional workloads
Tip
The RTX A5000 NVLink pairing is underrated for this use case. NVLink creates a unified 48GB memory pool rather than two separate 24GB pools. This means models up to 48GB can load entirely across the two cards as if they were one, without PCIe bottleneck on cross-card operations. Used A5000 pairs can be found for under $1,000 total.
Cost-Per-GB Comparison
$/GB
~$19
~$21
~$42
~$52
~$63
~$129 The multi-GPU path wins on cost-per-GB, but single-card wins on bandwidth and simplicity. For pure inference (no training, no batch processing), the A6000 or A5000 NVLink pair is the most practical 48GB configuration.
What the 48GB Floor Means for Your Hardware Roadmap
If you're running a 24GB card today, you're not obsolete — the 70B class models that defined 2024-2025 still run well on 24GB. But the open-weight frontier has moved up a tier. Mistral Small 4, Nemotron 3, and the next generation of frontier MoE models will all need more.
The upgrade path depends on your timeline:
- Next 6 months: Used RTX A5000 pair or dual 3090 setup gets you to 48GB at minimum cost
- Next 12 months: RTX 5090 at 32GB is the consumer single-GPU option (when it's more available); two 5090s would give 64GB
- Long term: 96GB+ dual-GPU setups running the full 120B MoE tier will likely become more accessible as the current workstation generation ages into the used market
The market is moving toward 48GB as the consumer enthusiast baseline the same way 24GB became the standard for serious local AI work in 2024. Position your hardware accordingly.
FAQ
What GPU gives you 48GB VRAM without going multi-GPU? The NVIDIA RTX 6000 Ada Generation (48GB) is the main single-GPU option at ~$6,000 new. Used NVIDIA A6000 48GB cards run $2,000–$3,000. For a multi-GPU path, two RTX 3090s (48GB total) cost around $1,000 used but require PCIe bandwidth management.
Is 48GB VRAM necessary or just ideal? It's the floor for running the current generation of top-tier open models at Q4 without multi-GPU. At 24GB you can run these models with aggressive quantization or split across RAM, but throughput drops significantly. 48GB is where you stop fighting VRAM and start running models properly.
What model sizes fit in 48GB VRAM? At Q4: 70B dense models fit cleanly (~40GB), 120B MoE models like Nemotron 3 Super and Mistral Small 4 fit (~50–55GB), most 70–130B active parameter models run without offloading. At Q8: 30–34B models fit cleanly, 70B dense needs a tight fit at ~75GB (doesn't fit).