Rent an RTX A6000 for 48 GB ECC at workstation pricing. The default pick when 24 GB runs out — 34B FP16 inference single-card at ~1,000 tok/s, Hunyuan-Video 720p production keeping T5-XXL resident, NVLink-paired 96 GB unified pool for 70B QLoRA via FSDP. Billed per-minute, paid in BTC, USDT/USDC or CLORE. Studio-friendly pricing for production work that doesn't need full HBM datacenter bandwidth.
A6000 is the workstation-grade 48 GB card — twice the memory of a 3090, all with ECC, all on certified drivers. Mid-tier in price, no-nonsense in production.
When you need more than 24 GB but the workload is not bandwidth-bound, the A6000 is the spec to pick. Runs 34B FP16 single-card, 8K Unreal cinematics, ANSYS CFD, and Blender scenes that exhaust 24 GB — at roughly half the rental price of an A100 80GB.
48 GB is enough for production-scale Houdini scenes, Blender Cycles with full geometry, and 8K video pipelines. OptiX accelerated.
48 GB hosts 13B FP16 + 7B FP16 on the same card with batched serving. ECC catches the bit flips that crash long-running endpoints.
When 24 GB is a constraint and you don't need HBM bandwidth, A6000 is the answer. The cheapest path to a single-card 48 GB workstation on the marketplace.
// prices are spot-market lows · refreshed every 60 s
Every server is priced by its host. These are the live floors across the marketplace — you'll see hundreds of variants once you're in.
No sales call. No quota request. No three-week procurement. The first four commands are all you need.
Filter the marketplace by RTX A6000, country, GPU count, reliability score, network speed.
Choose a Docker image — PyTorch, vLLM, ComfyUI, Blender — or paste your own.
You get a public endpoint, an SSH key, and Jupyter on port 8888 in under 90 s.
Per-minute billing rounds to the second. Stop the instance and the meter stops with it.
For 34B FP16 single-card inference, full-precision LoRA on 70B with FSDP across 2 cards, Unreal cinematics at 8K, and Blender scenes that exhaust 24 GB. The default pick when you need >24 GB but aren't paying H100 rates.
ECC memory catches single-bit errors silently in flight - mandatory for production CAD pipelines, V-Ray and Octane farms, regulated medical or financial ML, and any research where bit-flip integrity affects results. Pro cards (A4000/A5000/A6000/RTX 6000 Ada/A40) also carry ISV certifications consumer cards do not. If your client SLA references ECC or ISV validation, the consumer 4090 disqualifies.
The NVIDIA RTX A-series and RTX 6000 Ada carry full ISV certifications: V-Ray, Octane, SolidWorks, Rhino, DaVinci Resolve, ANSYS, COMSOL, and the Adobe Creative Cloud chain. Consumer Ada cards (4090/5090) are not on those lists. If your renderer's support matrix excludes GeForce, you need a pro card - which is exactly what CLORE.AI lists in this tier.
Yes - the A5000 and A6000 expose NVLink in pairs (no Switch fabric), giving 112 GB/s peer bandwidth and unified memory across two cards (48 GB on A5000 pair, 96 GB on A6000 pair). Filter by 'NVLink' in the marketplace to find listings. The RTX 6000 Ada and A40 do not have NVLink connectors but pair via PCIe with FSDP.
Pro cards (A6000 / RTX 6000 Ada / A40) give you 48 GB ECC at one-quarter to one-third the rental price of an A100 80GB and one-fifth of an H100. You give up HBM bandwidth and FP8 tensor cores, but for production rendering, virtual workstations, and 13B-34B inference under ECC the pro tier hits the price-performance sweet spot.
The cards themselves run cooler and quieter at lower TDP - A4000 is single-slot 140W, A5000 is dual-slot 230W, A6000 is 300W with a blower-style cooler designed for rack airflow. CLORE.AI is a remote rental platform, so the noise question only applies to your own studio if you're hosting; pro cards are explicitly the quieter pick there.
48 GB ECC at 300 W — the workstation default for 34B inference, 8K VFX, and Blender scenes that exceed 24 GB.
48 GB fits 34B FP16 weights plus KV cache for moderate concurrency — no offload, no model splitting.
Read the guide →48 GB lets Hunyuan keep T5-XXL + transformer + VAE all resident — lower latency than 24 GB cards with offload.
Read the guide →NVLink pair gives 96 GB unified pool — fits Llama-3 70B QLoRA without offloading optimizer state.
Read the guide →Side-by-side specs across the pro tier. Click any row to see that GPU.
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