48 GB class · Runpod
RTX A6000
Tight memory estimate · 1.85 GiB remaining
$0.59/ single-GPU instance-hour · USD
$59.00 for 100 hours · compute only
Available at observation ·
View this quote & conditions →Check Runpod pricing ↗Hardware decisions for AI workloads
Which GPU do you need
for a 70B LLM?
Choose your precision, context and concurrency. Estimate memory with actual Llama 3.3 70B files, then compare single-GPU rental options and cards for your business.
Size the workload first.
GPU capacity labels use a conservative decimal-GB budget here: 1 GB = 10⁹ bytes, while 1 GiB = 2³⁰ bytes. Check actual free VRAM before deployment.
Estimated GPU memory
42.85 GiB
46.01 GB · Q4_K_M · 4,096 tokens per sequence · 1 concurrent
- Weight files
- 39.60 GiB
- FP16 KV cache
- 1.25 GiB
- Runtime reserve
- 2.00 GiB
Single-GPU rental shortlist
48 GB class and up for this estimate.
One lowest collected hourly quote per memory class, up to three classes. 100 billable hours in this planning period. Memory qualification is an estimate; throughput and deployment compatibility are untested.
80 GB class · Runpod
A100 PCIe
Within memory estimate · 31.66 GiB remaining
$1.19/ single-GPU instance-hour · USD
$119.00 for 100 hours · compute only
Available at observation ·
View this quote & conditions →Check Runpod pricing ↗96 GB class · Runpod
RTX PRO 6000 Blackwell Server Edition
Within memory estimate · 46.56 GiB remaining
$2.49/ single-GPU instance-hour · USD
$249.00 for 100 hours · compute only
Available at observation ·
View this quote & conditions →Check Runpod pricing ↗Selected Runpod quotes only, not a market-wide price comparison. Stock is dated, not a reservation. Storage, startup and idle time, transfers, tax and other charges need separate budgeting.
Compare all 13 qualifying rental quotes
| GPU | Memory class | Compute / hour | 100 hours | Quote observed |
|---|---|---|---|---|
| RTX A6000 | 48 GB | $0.59 | $59.00 | 9 Nov 2026 |
| RTX 6000 Ada | 48 GB | $0.99 | $99.00 | 9 Nov 2026 |
| L40S | 48 GB | $1.09 | $109.00 | 9 Nov 2026 |
| A100 PCIe | 80 GB | $1.19 | $119.00 | 8 Nov 2026 |
| A100 SXM | 80 GB | $1.39 | $139.00 | 8 Nov 2026 |
| A100 PCIe | 80 GB | $1.79 | $179.00 | 9 Nov 2026 |
| A100 SXM | 80 GB | $1.79 | $179.00 | 9 Nov 2026 |
| RTX PRO 6000 Blackwell Server Edition | 96 GB | $2.49 | $249.00 | 9 Nov 2026 |
| H200 SXM | 141 GB | $3.59 | $359.00 | 8 Nov 2026 |
| H100 SXM | 80 GB | $3.99 | $399.00 | 9 Nov 2026 |
| H200 SXM | 141 GB | $5.29 | $529.00 | 9 Nov 2026 |
| B200 | 180 GB | $7.99 | $799.00 | 9 Nov 2026 |
| B300 | 288 GB | $8.99 | $899.00 | 9 Nov 2026 |
Buying instead? 3 reviewed cards meet this memory estimate
These are hardware candidates, not current purchase offers. Seller price and stock have not been collected. Check the exact board, power, cooling, host memory, driver and engine requirements before procurement.
RTX 6000 Ada Generation
48 GB class · Tight memory estimate
Inspect specifications & purchase evidence →RTX A6000
48 GB class · Tight memory estimate
Inspect specifications & purchase evidence →RTX PRO 6000 Blackwell Workstation
96 GB class · Within memory estimate
Inspect specifications & purchase evidence →The storage calculation
Why 70B at 4 bits is not the whole VRAM budget.
For exactly 70 billion parameters, ideal weight bytes = parameters × bits per parameter ÷ 8. This first table describes weight storage only.
| Precision | Bytes / parameter | Ideal weights · GB | Ideal weights · GiB |
|---|---|---|---|
| FP32 | 4 | 280 GB | 260.77 GiB |
| FP16 / BF16 | 2 | 140 GB | 130.39 GiB |
| INT8 | 1 | 70 GB | 65.19 GiB |
| INT4 | 0.5 | 35 GB | 32.60 GiB |
The reviewed Q4_K_M file is 42.52 GB (39.60 GiB), not the idealized 35 GB. Real formats include quantization metadata and can use mixed precision; “70B” is a model-size label. Use actual file sizes and the model's architecture for planning.
Inspect the model files and calculation
Estimated bytes = weight-file bytes + KV bytes + runtime reserve. KV bytes = 2 × 80 layers × 8 KV heads × 128 head dimension × total context tokens × concurrent sequences × 2 bytes for FP16.
This is a conservative full-context cache scenario without prefix sharing or cache quantization. Engines can allocate memory differently. Nominal discrete-GPU labels use a conservative decimal-GB budget across the GPU planners. Apple unified-memory pages use a separate system allowance. Check actual free memory for your deployment.
| Reviewed artifact | All weight files | Size |
|---|---|---|
| Q4_K_M | 1 file | 42.52 GB · 39.60 GiB |
| Q8_0 | 2 files | 74.98 GB · 69.83 GiB |
| F16 | 4 files | 141.12 GB · 131.43 GiB |
Pinned GGUF files ↗ · revision b6c5c9f176f3279204034e1d16d393105e95cb88 · checked 8 Nov 2026.
Pinned architecture configuration ↗ is a public Unsloth mirror for this model. Publisher model and license ↗.
Hugging Face memory-estimation guidance ↗ distinguishes model loading from inference requirements. llama.cpp GPU build documentation ↗ covers backend setup. We have not run these deployment or speed tests.
Before you choose a GPU.
Can a single RTX 4090 or RTX 5090 run this 70B model?
The reviewed Q4_K_M weight files alone exceed the 24 GB and 32 GB capacity labels of those cards. They do not fit this fully GPU-resident scenario. More aggressive quantization, CPU offload and smaller models are separate decisions with their own quality and speed trade-offs.
Are two 24 GB GPUs the same as one 48 GB GPU?
No automatic memory pooling is assumed. A multi-GPU setup needs an engine that can split the model, sufficient memory on each device, and suitable interconnect and host resources. Two separate single-GPU rental quotes do not establish a colocated two-GPU instance. This page qualifies single-GPU capacity only.
Does meeting this memory estimate mean the GPU is ready for production?
It gives you a capacity shortlist. Check your exact model files, engine and CUDA support, actual free VRAM, request pattern, latency and throughput. Run a representative test before buying or committing to long-term capacity. These numbers are for inference, not training or fine-tuning.
Do these figures apply to every 70B model?
The storage table is arithmetic for exactly 70 billion parameters. The interactive planner uses the pinned Llama 3.3 70B artifacts and architecture only. Other models, formats, KV cache layouts and serving engines need their own inputs. Changing precision does not establish equivalent task quality.
Compare other models in the LLM VRAM calculator →
Put the capacity choice into your operating budget.
Compare buying, renting and API use →Check Ollama context and allocation →Send a hardware or pricing correction →