Open-Source LLM VRAM & Hardware Sizer
Input your model parameter size ($P$), quantization precision ($Q$), and target context window to compute the exact memory footprint: Model Weights + KV Cache + CUDA Overhead. Matches configurations against 19 cloud accelerators.
Deploy 1x to 8x H100 SXM5 & RTX 4090 Pods
Launch continuous batching vLLM instances in under 60 seconds with pre-installed CUDA 12 and TensorRT-LLM.
Open-Source LLM VRAM & Multi-GPU Sizer
Calculates weights memory, dynamic KV context expansion, and CUDA kernel runtime overhead.
Total VRAM Required: 50.5 GB
GPU Hardware Compatibility & Speed Matrix
Live hardware matching: Showing setups capable of serving this 70B model at 50.5 GB total VRAM.
NVIDIA B200 192GB
Blackwell enterprise powerhouse delivering 2.5x the inference throughput of H100 with massive 192GB HBM3e.
NVIDIA H200 141GB SXM5
Equipped with 141GB HBM3e at 4.8 TB/s bandwidth, ideal for high-throughput serving of 70B+ LLMs without pipeline parallelism.
NVIDIA H100 80GB SXM5
The world benchmark for AI training and low-latency FP8 inference with Transformer Engine.
NVIDIA H100 80GB PCIe
PCIe form factor H100 offering high density deployment in standard server racks with 350W TDP.
NVIDIA A100 80GB SXM4
Proven workhorse for fine-tuning and inference with NVLink interconnect and 2 TB/s bandwidth.
NVIDIA A100 80GB PCIe
High memory capacity PCIe card for reliable batch inference and medium-scale multi-GPU clusters.
2x NVIDIA A100 40GB PCIe
Cost-effective enterprise GPU for smaller LLMs (8B, 14B) and heavy embedding generation pipelines.
2x NVIDIA L40S 48GB
Ada Lovelace architecture powerhouse optimized for multimodal AI, graphics rendering, and 70B quantization serving.
4x NVIDIA L4 24GB
Low-power 72W single-slot server accelerator for video AI pipelines and lightweight model inference.
2x NVIDIA RTX 6000 Ada 48GB
Premier professional workstation GPU with 48GB ECC VRAM, suited for local enterprise fine-tuning and 70B inference.
2x NVIDIA RTX A6000 48GB
Ampere generation 48GB workstation GPU offering incredible cloud rental price-to-VRAM value.
2x NVIDIA RTX 5090 32GB
Next-gen consumer flagship with 32GB GDDR7 and nearly 1.8 TB/s bandwidth for blazing-fast local open weights.
4x NVIDIA RTX 4090 24GB
The golden standard for AI hackers and developers. Over 1 TB/s memory bandwidth for sub-$0.50/hr cloud rentals.
4x NVIDIA RTX 4080 Super 16GB
Sub-$1000 GPU capable of running quantized 8B and 14B models locally with high token generation speeds.
4x NVIDIA RTX 3090 24GB
The budget community king: 24GB VRAM with NVLink support available at under $0.25/hr in community clouds.
AMD Instinct MI300X 192GB
Massive 192GB HBM3 memory pool with 5.3 TB/s bandwidth, running Llama 3 405B on a single node via ROCm/vLLM.
Apple Mac Studio M4 Max 128GB
Unified memory architecture allowing up to 120GB allocation to LLMs locally with whisper-quiet 140W power draw.
Apple Mac Studio M3 Ultra 192GB
Massive 192GB unified RAM desktop powerhouse capable of running 70B models at full FP16 or quantized 405B models locally.
Apple MacBook Pro M4 Max 128GB
Ultimate portable workstation running 70B parameter models entirely offline on battery.
The Physics of LLM Inference Memory
Many teams purchase or rent GPUs based solely on model weight sizes, only to experience out-of-memory (OOM) errors as soon as user traffic spikes or long documents enter the context window.
1. The Weights Footprint
At 16-bit precision (FP16 or BF16), each parameter requires 2 bytes. A 70-billion parameter model requires:
70B × 2.0 Bytes × 1.05 (architecture overhead) = 147 GB VRAMAt 4-bit quantization (AWQ or GPTQ), each parameter is compressed to 0.5 bytes, bringing the model down to ~38.5 GB, which fits comfortably on a single 48GB GPU (like NVIDIA L40S or RTX 6000 Ada) or a dual 24GB RTX 4090 setup.
2. The KV Cache Explosion
Attention mechanisms must store previous key and value vectors for every token in the active context. The KV cache grows linearly with context length and number of concurrent user requests.
KV_Cache = 2 × n_layers × n_heads × d_head × tokens × precision × batch_sizeServing a 128k context window on Llama 3.3 70B with multiple concurrent requests can easily consume an additional 30GB to 60GB of VRAM solely for KV cache buffers!
Frequently Asked Hardware Questions
Can I run Llama 3.3 70B on a single RTX 4090 (24GB)?
No. Even at 4-bit quantization, Llama 70B weights consume ~38.5 GB. You need at least 2x RTX 4090s (48GB combined VRAM) or an RTX 6000 Ada (48GB) to run 70B with modest context.
Why is Memory Bandwidth critical for token speed?
Autoregressive generation is memory-bandwidth bound, not compute bound. Generating 1 token requires reading all model weights from VRAM into the compute cores. The theoretical token speed limit is roughly Memory Bandwidth (GB/s) / Weights Size (GB).