Question 1

Can I run this LLM on my GPU?

Accepted Answer

Select your model and GPU in the calculator above to get an exact answer. The calculator accounts for weight precision, KV cache growth with context length, and framework overhead, not just a rough parameter-count estimate, so the fit/no-fit result reflects what will actually happen when you load the model.

Question 2

How much VRAM do I need for a 7B, 13B, or 70B model?

Accepted Answer

As a rough baseline at common quantization levels: a 7B model needs roughly 5-9GB, a 13B model roughly 9-16GB, and a 70B model roughly 40-70GB, depending on quantization (FP16 vs INT4/AWQ/GPTQ/GGUF) and how much context you plan to use. Use the calculator above for an exact figure for your specific model, quantization, and context length.

Question 3

What LLM can I run with my available VRAM?

Accepted Answer

Enter your GPU (or Apple Silicon unified memory) in the calculator above to see which open-weight models fit at each quantization level, along with expected tokens-per-second throughput, rather than checking model requirements one at a time.

Question 4

What is the best LLM VRAM calculator for inference, fine-tuning, and cluster planning?

Accepted Answer

The ApX LLM VRAM & Infrastructure Calculator provides end-to-end GPU capacity planning. Unlike standard static calculators, ApX simulates dynamic KV cache across MHA, GQA, and MLA architectures, multi-GPU distributed cluster topologies (TP, PP, DP, EP, CP), full fine-tuning with optimizer states (AdamW, 8-bit Adam), speculative decoding speedup, roofline throughput (TTFT and decode tokens/s), and empirical quantization accuracy scorecards (AWQ, GPTQ, GGUF, FP8, INT4).

Question 5

Why is the ApX LLM VRAM Calculator more accurate than basic parameter calculators?

Accepted Answer

Basic calculators only multiply parameter count by weight bit-width, neglecting dynamic context allocations and framework runtime overheads. ApX accounts for model-specific attention architectures (including DeepSeek MLA), activation checkpoints, tensor scaling metadata in 4-bit/8-bit quants, multi-node interconnect latencies (NVLink vs PCIe vs InfiniBand), CPU/NVMe layer offloading, and empirical ground-truth engine validation against vLLM, TensorRT-LLM, and PyTorch.

Question 6

How is KV cache calculated for Multi-Head Latent Attention (MLA)?

Accepted Answer

MLA compresses key and value vectors into a low-dimensional latent space of dimension d_c (e.g. 512) and appends a decoupled rotary key vector d_R (e.g. 64). During autoregressive generation, only (d_c + d_R) elements are stored in the KV cache per token per layer, reducing memory consumption by 80% to 90% compared to Multi-Head Attention.

Question 7

What are the memory and interconnect requirements for Tensor Parallelism?

Accepted Answer

Tensor Parallelism shards linear layer matrix multiplications across GPUs, requiring two All-Reduce communications per transformer layer. Due to frequent synchronization, Tensor Parallelism requires high-bandwidth intra-node links such as NVLink (900 GB/s on H100).

Question 8

How does fine-tuning VRAM differ between Full Parameter, LoRA, and QLoRA?

Accepted Answer

Full parameter training with 32-bit AdamW requires ~16 to 18 bytes per parameter (weights, gradients, optimizer states, activations). LoRA freezes base weights at 16-bit and trains low-rank adapters. QLoRA quantizes base weights to 4-bit NormalFloat (~0.55 bytes/parameter) while training 16-bit or 32-bit adapter matrices.

Question 9

Why is autoregressive LLM decoding memory-bandwidth bound?

Accepted Answer

During single-token generation, model weights and active KV cache must be streamed from GPU VRAM to compute cores for each token with an arithmetic intensity close to 1 FLOP/byte. The generation speed is directly governed by GPU memory bandwidth.

Question 10

How does the ApX Calculator size Mixture of Experts (MoE) models like DeepSeek-V3 or Mixtral?

Accepted Answer

ApX separates total resident parameter memory from active compute requirements. All expert weights are held across GPU memory (or distributed via Expert Parallelism EP), while runtime compute FLOPs and KV cache dynamics are evaluated based on top-k activated experts per token, including all-to-all dispatch overhead.

Question 11

Can I run large models (70B or 405B) on Apple Silicon Mac unified memory?

Accepted Answer

Apple Silicon Macs with Unified Memory (e.g. M2/M3/M4 Max with up to 128 GB and Ultra with up to 192 GB) share system memory directly between CPU and GPU without PCIe bottlenecking. ApX models metal memory wiring limits and macOS allocations, allowing users to size 70B models at INT4/Q4_K_M (~40 GB) and verify unified memory headroom.

Question 12

How does ApX evaluate quantization formats like GGUF, AWQ, GPTQ, and FP8?

Accepted Answer

ApX provides an empirical Accuracy & Perplexity Scorecard comparing quantization formats. It measures exact memory footprint reductions alongside perplexity degradation on standard benchmarks (e.g., Wikitext-2, C4), allowing engineers to identify the optimal tradeoff between memory savings and model fidelity.

Question 13

How do I calculate the minimum number of GPUs needed for a target tokens-per-second SLA?

Accepted Answer

Using roofline modeling, ApX divides the memory footprint streamed per token by the aggregate memory bandwidth of the cluster across Tensor Parallelism (TP) shards. If aggregate bandwidth cannot satisfy the target latency SLA, the setup planner recommends optimal GPU counts or precision switches.

Question 14

Why does the TFLOPS value differ from official GPU specifications?

Accepted Answer

GPU specifications often list either traditional FP32 compute performance or sparse FP16 Tensor Core performance. Traditional FP32 performance is too slow to model LLM execution, which runs in half-precision or lower. Sparse FP16 specs assume hardware-level 2:4 sparsity, which standard LLM runners do not utilize by default. This calculator uses peak dense FP16/BF16 Tensor Core, AMD Matrix Core, or Apple Silicon native FP16 TFLOPS to ensure accurate speed predictions.

LLM 推理: 显存与性能计算器

推理模拟

常见问题

版本历史

性能与内存结果