Is 48GB of VRAM Enough for Local AI in 2026?

Bottom Line (August 2026)

• Yes — 48GB is the first tier that runs a dense 70B. ~40GB of Q4 weights fit. That is the reason to buy it.
• The context you get with that 70B is about 19K tokens, not 128K. Roughly 6GB is left after the weights, and a 70B burns 0.305 GiB of FP16 cache per 1K tokens.
• A 70B’s full 128K KV cache is exactly 40 GiB. The same size as its own Q4 weights. Full-context 70B is an 80GB-class problem.
• Below 70B, 48GB is luxurious. The 27B and 32B classes run at high quantization with genuinely long context.
• Routes vary by 3x on price. ~$2,000–2,600 for two used 3090s, $2,600–3,800 for a used A6000, $5,600–6,250 for a new RTX PRO 5000.

What Fits at 48GB

Weights only. Context comes on top, and that is this page’s whole argument.

The step from 32GB is real and it is a step in class, not in quantization. 32GB cannot run a dense 70B at any quantization worth using. 48GB can. That distinction is the honest sales pitch for the tier, and every review makes it.

What no review makes is the next section.

The Context Ceiling — Where 48GB Actually Stops

Take the published config.json for Llama-3.3-70B-Instruct: 80 hidden layers, 8 key-value heads, hidden size 8192 across 64 attention heads — so a head dimension of 128 — and max_position_embeddings of 131,072.

Apply the standard KV cache formula, the same one derived on how much VRAM for 128K context:

bytes/token = 2 (K and V) x layers x kv_heads x head_dim x bytes_per_value
            = 2 x 80 x 8 x 128 x 2
            = 327,680 bytes per token

That is 0.3125 MiB per token, or 0.305 GiB per 1,000 tokens. It is arithmetic on published config fields, not a benchmark.

Now spend a 48GB card. Call ~46GB usable after driver and framebuffer overhead — check your own nvidia-smi, it varies by a gigabyte or two. Subtract ~40GB of Q4 weights and about 6GB remains:

And here is the number that reframes the tier. Running that 70B at its own advertised 131,072-token window, at FP16:

327,680 bytes x 131,072 tokens = 42,949,672,960 bytes = exactly 40 GiB

A 70B’s full context cache is the same size as its Q4 weights. Weights plus full cache is ~80GB. So the model that justifies the 48GB tier needs a 96GB card to run the way its model card describes it. We have not seen this stated on any 48GB buying guide, and it is the single most useful thing to know before spending $2,600.

Compare the shape at 32GB, where a dense 32B leaves ~13GB and gets ~52K tokens. The 48GB card runs a bigger model with less context than the 32GB card. That is not a defect of the card. It is what happens when KV cache scales with layer count and you move from 64 layers to 80.

The lever is KV cache quantization. At q8 the numbers roughly double and 39K is a workable agent session. Treat it as a required setting at this tier, not an optimization.

Where 48GB Is Genuinely Comfortable

Drop below 70B and the picture inverts. A dense 32B at Q4 uses ~19GB and leaves ~27GB, which is about 88K tokens of FP16 cache — a long agent session with no cache tricks at all. A mixture-of-experts model with 4 key-value heads gets multiples of that.

So 48GB has two distinct personalities:

• As a 70B machine: capable, context-constrained, needs q8 cache to be pleasant.
• As a 32B machine: the most comfortable single-GPU experience available short of a workstation card.

Most people buy it for the first and end up living in the second.

The Four Routes to 48GB, and What They Cost

All prices are US street, as of August 2026, from our hardware price reference. The 2026 DRAM and GDDR7 shortage moves these week to week — check listings before you commit.

Route 1 — Two used RTX 3090s, ~$2,000–2,600. The cheapest path and the most work. Used 3090s run $1,000–1,300 each, which is the number most guides still get wrong; “$650–750” has not been true for a long time. Add a 1000W-plus supply and a board with two spaced PCIe slots.

EVGA GeForce RTX 3090 24GB — two of these are the 48GB floor. The 3090 is also the last GeForce card with NVLink, which matters only if you run vLLM tensor parallel; see is NVLink worth it.

Route 2 — One used RTX A6000 48GB, $2,600–3,800. The same capacity in one slot at 300W, with no model splitting and no dual-PSU thinking. Supply is enterprise lease returns, so condition varies widely and so does price.

PNY NVIDIA RTX A6000 48GB GDDR6 — our default recommendation for this tier if the price lands near the bottom of the range. Full detail in best local LLM on an RTX A6000.

Route 3 — New RTX PRO 5000 Blackwell 48GB, $5,600–6,250. Current-generation silicon, a warranty, far more bandwidth than the A6000, and roughly double the price. Note that a 72GB RTX PRO 5000 also exists — do not conflate the two when you shop.

PNY NVIDIA RTX PRO 5000 Blackwell 48GB — buy this if you need the card new, supported and fast, and the budget allows it.

Route 4 — Unified memory instead. A Mac or a Strix Halo box gives you 48GB or more of shared memory at far lower cost per gigabyte and far lower bandwidth. Different tradeoff entirely — see Mac Studio vs an RTX workstation.

Whichever route you pick, the dual-GPU ones need real power. A four-figure GPU pair behind a marginal supply is the one place to not economise:

MSI MAG A1200PLS 1200W 80+ Platinum ATX 3.1 — native 12V-2x6, sized for a two-card build. More detail in what PSU for a local AI rig.

The Honest Case Against 48GB

It is sized for 70B weights and not for 70B context. That is the whole critique. If your reason for the tier is “I want to run a 70B properly,” check whether “properly” means 19K tokens to you. For many agent workloads it does not.

Two 3090s cost about what one RTX 5090 costs, and are slower per card. The 5090 gives you 32GB at 1,792 GB/s. Two 3090s give you 48GB at 936 GB/s each with a PCIe hop between halves. Capacity or speed — see dual RTX 3090 vs RTX 5090.

The mixture-of-experts trend keeps undercutting the tier from below. Laguna S 2.1 delivers frontier-adjacent coding at 39.7GB, and MoE models with few key-value heads get far more context from the same memory than any dense 70B will. A large fraction of what people want a 70B for is now available under 40GB with better context behaviour.

Where 48GB is unambiguously right: you specifically want dense 70B-class output, you will turn on q8 KV cache, you want it on one card, and you do not want to spend five figures. That is a genuine and common position, and the used A6000 serves it well.

The Decision

See Also

• Is 32GB of VRAM Enough in 2026? — the tier below, and the same question
• Is 16GB of VRAM Still Enough in 2026? — where this series starts
• Best 48GB VRAM Setup for Local LLMs — how to reach 48GB, route by route
• How Much VRAM for 128K Context? — the KV cache formula used above
• KV Cache Quantization: q8 vs q4 — how to roughly double every context number here
• Dual RTX 3090 vs RTX 5090 — 48GB slow against 32GB fast
• Best Local LLM on an RTX A6000 — the single-card 48GB route in detail
• RTX PRO 6000 Max-Q vs Workstation Edition — the 96GB tier above

Sources

• Llama-3.3-70B-Instruct config.json: 80 hidden layers, 64 attention heads, 8 key-value heads, hidden size 8192, max_position_embeddings 131072, bfloat16 — read from the published model configuration
• KV cache figures are our arithmetic on those fields using the formula derived in how much VRAM for 128K context. They are not benchmarks.
• Model weight footprints are our own published figures, kept consistent across the 16GB, 32GB and 48GB tier pages
• Prices, all as of August 2026, from our hardware price reference: used RTX 3090 $1,000–1,300 (ResalePrices eBay-US active listings, gpudojo, bestvaluegpu); used RTX A6000 48GB $2,600–3,800 (gpudojo Jul 2026, used-A6000 guides); RTX PRO 5000 Blackwell 48GB $5,600–6,250 (Pangoly price history, GPU Poet Jun 2026)