VPS Arena

Run Llama 3.3 70B on a VPS.

September 2026 · 15 of 280 plans fit · re-ranked daily

Near-frontier quality from a dense 70B; needs a 64 GB box and patience on CPU. On a CPU-only VPS it needs about 46.5 GB of RAM: 42.5 GB of Q4_K_M weights, 2.5 GB of KV cache for an 8,192-token context and 1.5 GB for the OS and runtime. 15 of the 280 plans in our index fit; the cheapest comfortable pick is Contabo's Cloud VPS 16 · 16 vCPU · 64 GB at $43/mo, streaming an estimated 0.6–1.1 tok/s.

RAM needed · CPU inference

46.5 GB

weights · Q4_K_M
42.5 GB
KV cache · 8K context
2.5 GB
OS + runtime headroom
1.5 GB

Other quants: Q8_0 weights 75 GB (near-lossless, about half of F16); F16 141.1 GB. Comfortable = 20% headroom over the total.

Model card

Size
71B parameters
Context
128K tokens
Kind
chat
Released
2024-12
License
Llama 3.3 Community License

Facts fetched from Hugging Face on 3 Sept 2026: exact GGUF file sizes, KV geometry from the GGUF header · 627,558 downloads.

ollama run llama3.3:70bmodel card ↗Meta

Best VPS plans for Llama 3.3 70B

ranked by estimated tokens/s per dollar, comfortable fits first

  1. 1
    ContaboCloud VPS 16 · 16 vCPU · 64 GB

    16 vCPU shared · 64 GB RAM · 500 GB SSD

    runs comfortably~0.6–1.1 tok/s$43/mo
  2. 2
    netcupRS 8000 G12 · 16 dedicated cores · 64 GB

    16 vCPU dedicated · 64 GB RAM · 2048 GB NVME

    runs comfortably~0.8–1.7 tok/s$69.70/mo
  3. 3
    netcupVPS 8000 G12 · 16 vCore · 64 GB

    16 vCPU shared · 64 GB RAM · 2048 GB NVME

    runs comfortably~0.6–1.1 tok/s$46.83/mo
  4. 4
    ContaboCloud VPS Plus 16 · 16 vCPU · 64 GB

    16 vCPU shared · 64 GB RAM · 750 GB NVME

    runs comfortably~0.6–1.1 tok/s$91.81/mo
  5. 5
    ContaboCloud VPS Plus 18 · 18 vCPU · 96 GB

    18 vCPU shared · 96 GB RAM · 900 GB NVME

    runs comfortably~0.6–1.1 tok/s$115.06/mo
  6. 6
    ScalewayBASIC2-A16C-64G

    16 vCPU shared · 64 GB RAM · 0 MB

    runs comfortably~0.6–1.1 tok/s$233.82/mo

Speed = effective memory bandwidth ÷ active weight bytes (4 GB/s per shared vCPU, 6 per dedicated), shown as a band. Real numbers depend on the host CPU generation, AVX-512/AMX support and how noisy the neighbours are — treat these as order-of-magnitude.

Or buy hardware · 5 reference machines fit

all machines →
  • Framework Desktop (Ryzen AI Max+ 395, 64 GB)64 GB · 256 GB/s · ~3.1–4.8 tok/s · tight$1,959= 46 mo of VPS
  • Mac mini (M5 Pro, 64 GB)64 GB · 307 GB/s · ~3.8–5.8 tok/s · tight$2,299= 54 mo of VPS
  • Framework Desktop (Ryzen AI Max+ 395, 128 GB)128 GB · 256 GB/s · ~3.1–4.8 tok/s$3,449= 80 mo of VPS
  • NVIDIA DGX Spark (128 GB)128 GB · 273 GB/s · ~3.4–5.1 tok/s$4,699*= 109 mo of VPS
  • Mac Studio (M5 Ultra, 96 GB)96 GB · 1200 GB/s · ~15–23 tok/s$5,499= 10+ yrs of VPS

Buy · per month

$59.09

$54.42 hardware + $4.67 power

Rent · per month

$43

Contabo Cloud VPS 16 · 16 vCPU · 64 GB

Break-even

The Framework Desktop (Ryzen AI Max+ 395, 64 GB) pays for itself after 51 months of replacing the VPS — and it streams an estimated 3.1–4.8 tok/s against the VPS's CPU-only pace.

* approximate: August 2026 US retail median rather than list price. Local speed = peak bandwidth × 0.7 efficiency (0.35 on CPU-only boards) ÷ active weight bytes; unified-memory machines are assumed to give models 75% of their RAM. GPU cards need a host PC that is not included in the price.

Frequently asked

How much RAM does Llama 3.3 70B need?
About 46.5 GB for CPU inference at Q4_K_M: 42.5 GB of weights, 2.5 GB of KV cache at 8,192 tokens of context, and 1.5 GB of headroom. Longer contexts need more KV cache (320 KB per token for this model).
What is the cheapest VPS that can run Llama 3.3 70B?
Contabo Cloud VPS 16 · 16 vCPU · 64 GB (64 GB RAM, 16 vCPU) at $43/mo excl. VAT runs it comfortably as of 5 Sept 2026. Contabo Cloud VPS 12 · 12 vCPU · 48 GB at $29.05/mo is a tight fit.
How fast will Llama 3.3 70B run on a VPS without a GPU?
Roughly 0.6–1.1 tok/s on the top pick. Token generation is bound by memory bandwidth — each token streams the full weights once — so more and dedicated vCPUs help; a GPU or Apple-silicon machine is 10–50× faster.
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