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Choose GPU memory from the model’s complete working set, not its parameter count alone. For inference, budget for weights, activations, the KV cache, runtime overhead, and every concurrent request. For training, add gradients, optimizer state, training activations, and batch data; the requirement is usually much larger. Eliminate any instance that cannot hold that working set with safety headroom, then compare memory bandwidth, tensor throughput, interconnect, CPU and host RAM, networking, storage, latency, throughput, quotas, and price. A 7B model may need about 14 GB just for FP16 weights, while a 70B model needs about 140 GB in FP16 or 35 GB with INT4-class weights. Those are weight-only figures, so the right instance is the smallest configuration that fits your actual workload and meets its speed target.
What exactly has to fit in GPU memory?
GPU sizing starts with an inventory of everything resident during the workload. AWS Prescriptive Guidance describes memory sizing as the step that establishes which instance sizes are viable before throughput and latency are evaluated. If the model and runtime requirements exceed an instance’s available memory, that instance cannot be used regardless of its computational performance.
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- Model weights: the parameters stored in FP16, BF16, FP8, INT8, INT4 or another format.
- Activations: intermediate tensors needed while processing each request.
- KV cache: attention keys and values retained for generated and, depending on the runtime, prefetched context.
- Runtime overhead: kernels, allocators, communication buffers, graph metadata and framework reservations.
- Concurrency: memory consumed by all requests that are in flight, not just one prompt.
A model that loads successfully for one short prompt can still run out of memory when context length, output length or concurrent sessions increase.
Training working set
Training keeps the weights plus gradients, optimizer state, training activations and batch data. Sequence length, micro-batch size, gradient accumulation, checkpointing strategy and optimizer choice change the peak. Parameter-efficient methods such as LoRA can reduce the trainable state, but the frozen model, activations and runtime still need room.
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How do I convert parameter count into weight memory?
Use this first-pass calculation:
Weight memory ≈ parameter count × bytes per parameter.
Then add memory for the rest of the working set and a safety margin. AWS published the following weight-only examples in 2025:
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|---|---|---|---|
| 7B | FP16 | 14 GB | Activations, KV cache, runtime overhead and concurrency |
| 13B | FP16 | 26 GB | Activations, KV cache, runtime overhead and concurrency |
| 70B | FP16 | 140 GB | Activations, KV cache, runtime overhead and concurrency |
| 70B | FP8/INT8 | 70 GB | Activations, KV cache, runtime overhead and concurrency |
| 70B | INT4/NVFP4 | 35 GB | Activations, KV cache, runtime overhead and concurrency |
These numbers are not capacity guarantees. For example, a 24 GB card cannot hold 13B FP16 weights because the weights alone are listed at 26 GB. A 70B INT4/NVFP4 model starts at 35 GB before any serving overhead, so a 40 GB card may be a tight experiment rather than a comfortable production target. Quantization changes weight storage, but it does not remove cache, activation or concurrency requirements.
How much headroom should I leave?
Do not plan to fill every advertised gigabyte. Framework reservations, allocator fragmentation, longer-than-average prompts, batch variation and a rise in concurrent requests can push a deployment over the limit.
Google Cloud’s 2025 practical bands are useful starting points:
| GPU memory band | Guidance from Google Cloud | Interpretation |
|---|---|---|
| 16 GB | Basic inference or very small-batch LoRA | Suitable only when the complete runtime remains small and concurrency is limited |
| 24 GB | Starting point for 4–7B fine-tuning | Validate sequence length, batch and adapter settings before committing |
| 40+ GB | Large batches or models in the 20B-plus range | Provides a larger operating envelope, but still requires a measured peak |
These are starting bands, not guarantees for every framework or model. Reserve capacity for the highest context and concurrency you intend to support, then test a worst-case batch rather than only an average request.
How does the KV cache change inference sizing?
The KV cache grows with the amount of conversation the server keeps available for attention. AWS notes that demand scales approximately linearly with input context length, generated output length, concurrent requests, the number of KV heads and layers, and head dimension.
Variables to record
- Maximum input context: the longest prompt or conversation the API will accept.
- Maximum output: the token budget the scheduler allows a request to generate.
- Concurrency: the number of active sequences, including requests waiting in a continuous-batching queue if the runtime allocates cache early.
- Architecture: KV-head count, layer count and head dimension.
- Scheduling policy: whether the server evicts, pages or reserves cache for future tokens.
Estimate a single-request peak, multiply it by your intended simultaneous requests, and add runtime overhead before selecting a card. If a workload has unpredictable traffic, use admission control or a cache limit so a burst cannot consume all free memory.
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What changes when I train or fine-tune a model?
Training requires more than loading the model. During a step, the GPU may hold gradients, optimizer state, activations for backpropagation and batch tensors in addition to weights. Full-parameter training therefore needs a substantially larger memory tier than inference at the same parameter count.
Full training
Record the optimizer, precision policy, global and micro-batch sizes, sequence length, gradient accumulation and checkpointing mode. Activation memory can dominate when sequences or batches grow, while optimizer state can dominate when every parameter is updated. Distributed data, tensor or pipeline parallelism can spread these structures, but communication buffers and synchronization add their own demand.
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Parameter-efficient fine-tuning
LoRA and similar adapters reduce the number of trainable parameters. Google Cloud’s 16 GB band describes only basic inference or very small-batch LoRA; its 24 GB band is a starting point for 4–7B fine-tuning. Treat those statements as entry points: a longer sequence, larger micro-batch, optimizer choice or evaluation pass can require the next memory tier.
Memory-saving techniques
- Use BF16, FP16, FP8, INT8 or INT4-class representations only when your kernels and quality targets support them.
- Reduce micro-batch size or sequence length before shrinking the model’s context contract.
- Use activation checkpointing when extra computation is preferable to storing every activation.
- Use gradient accumulation to reach an effective batch without keeping the full batch resident at once.
- Shard weights, gradients or optimizer state across GPUs when one card cannot hold the working set.
Each technique changes speed, numerical behavior or engineering complexity. Measure the resulting step time and validation quality instead of assuming that a smaller footprint is automatically faster or better.
Which GPU memory tiers are available?
Google’s GPU catalog lists several useful capacity tiers. The figures below are the listed GPU memory capacities; an instance may include one or more GPUs.
| Accelerator | Listed memory | Typical sizing implication |
|---|---|---|
| L4 | 24 GB GDDR6 | Small inference services, development and compact fine-tuning |
| A100 | 40 GB or 80 GB | Medium-to-large models, training and higher batch sizes |
| H100 | 80 GB | High-throughput training and serving |
| H200 | 141 GB | Large models or larger context and batch envelopes |
| B200 | 180 GB | Very large models and demanding distributed workloads |
| GB200 | 186 GB | Large-memory accelerator systems with advanced interconnects |
| GB300 | 279 GB | Largest listed capacity tier for especially memory-intensive jobs |
Higher-end systems are paired with NVLink meshes and much greater interconnect bandwidth. That matters when a model is sharded: the usable system is not merely the sum of capacities, because every forward or backward step may exchange tensors between GPUs.
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What AWS and Google Cloud instance options map to these tiers?
Cloud catalogs expose different capacities and names, so compare the actual configuration rather than the accelerator label alone. Google Compute Engine documents A3 High H100 configurations with 1, 2, 4 or 8 GPUs, providing 80–640 GB of aggregate GPU memory. It also documents A3 Ultra H200 and A2 Standard and A2 Ultra A100 configurations.
AWS positions G-series instances for graphics, AI/ML inference and single-node training, while P-series targets AI inference and training of large foundation models. AWS’s right-sizing examples include:
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| AWS example | GPU memory listed by AWS | Use the figure for |
|---|---|---|
| g6 L4 | 22 GB | Small inference and single-node workloads |
| g6e L40S | 44 GB | Larger single-GPU models and batches |
| g7e RTX PRO 6000 | 96 GB | High-memory single-GPU workloads |
| p5 H100 | 80 GB | Large-model training and inference |
| p5en H200 | 141 GB | Models needing more than an H100’s memory |
| p6-b200 B200 | 180 GB | Very large-memory accelerated workloads |
Google lists an L4 at 24 GB GDDR6, whereas the AWS g6 example lists 22 GB; treat those as provider-specific configurations, not a contradiction to resolve by arithmetic. Verify the live catalog for exact instance names, regional availability, quotas and prices before provisioning.
When do I need more than one GPU?
Use one GPU when the complete working set fits with headroom and its bandwidth and throughput satisfy the target. Move to two, four or eight GPUs when the model cannot fit on one card, when the required batch or concurrency exceeds one card, or when measured throughput justifies parallel execution.
Single-node multi-GPU
Single-node systems usually provide faster GPU-to-GPU links than networked nodes. Tensor parallelism splits layers or matrix operations; pipeline parallelism assigns groups of layers to different GPUs; data parallelism keeps a replica on each GPU and divides batches. The right method depends on model architecture, batch size and communication volume.
Multi-node scaling
Multi-node training and serving add network latency, bandwidth limits, placement constraints and more failure points. A nominal total such as eight 80 GB GPUs does not behave like one 640 GB memory pool unless the software can shard the model and communicate efficiently. Benchmark scaling efficiency at the intended sequence length and batch rather than extrapolating from a single-GPU run.
How should I compare performance after memory fits?
Memory capacity is the gate; performance comparisons come afterward. Evaluate these axes together:
| Decision axis | Questions to answer |
|---|---|
| Fit | Does total working-set memory plus safety margin fit the selected configuration? |
| Performance | What memory bandwidth and tensor throughput are available at the chosen precision? |
| Interconnect | How quickly can GPUs exchange tensors through NVLink or the host and network fabric? |
| Scale | Will the workload run on one GPU, 2/4/8 GPUs or multiple nodes, and how does efficiency change? |
| Serving behavior | What context length, concurrency, latency target and tokens-per-second rate must be sustained? |
| Training behavior | What batch, sequence length, optimizer, checkpointing policy and scaling efficiency are required? |
| Operations | Do CPU, host RAM, network, local storage, quota, region and capacity match the deployment? |
A faster GPU that cannot hold the model is not an option. Conversely, buying the largest-memory accelerator without checking bandwidth, interconnect or utilization can waste budget when a smaller card meets the service-level target.
How do I choose an instance step by step?
- Define the workload. Record training or inference, model version, precision, maximum context, output limit, batch or concurrency, latency target and throughput target.
- List every resident tensor. For inference, include weights, activations, KV cache and runtime allocations. For training, add gradients, optimizer state, training activations and batch data.
- Calculate weight memory. Multiply parameters by bytes per parameter and compare the result with the published examples or your model’s checkpoint size.
- Estimate dynamic memory. Size KV cache for the longest context and output at the intended concurrent request count. For training, measure peak memory at the planned sequence and micro-batch.
- Add headroom. Leave room for allocator behavior, framework buffers, traffic bursts and future context or batch increases.
- Eliminate infeasible tiers. Remove every single-GPU option that cannot hold the working set. Consider sharding only when the framework supports it and interconnect is adequate.
- Shortlist hardware. Compare memory bandwidth, tensor throughput, interconnect, GPU count, CPU, host RAM, network and local storage.
- Check cloud constraints. Confirm region, quota, on-demand or spot policy, capacity availability and current price for the exact instance.
- Benchmark the real shape. Test representative prompts or training batches, then a worst-case context, concurrency and failure-recovery scenario.
- Choose the smallest passing configuration. Select the option that fits with headroom and meets latency, throughput and reliability requirements without paying for unused capacity.
What do common sizing scenarios look like?
7B FP16 inference
The published weight estimate is 14 GB. A 16 GB tier leaves only a small remainder for activations, KV cache and runtime allocations, so it may work for short, low-concurrency requests but is not a comfortable default. A 24 GB L4-class option gives more room for cache and batching; validate the maximum context and concurrent sessions before production.
13B FP16 fine-tuning
Weights alone are listed at 26 GB, already above a 24 GB card. Full fine-tuning also needs gradients, optimizer state and activations, so move to a larger-memory configuration or change the precision, adapter method or sharding plan.
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70B serving
FP16 weights are listed at 140 GB, which generally implies a multi-GPU configuration such as two or more 80 GB-class accelerators, subject to runtime overhead and sharding efficiency. FP8/INT8 reduces the listed weight storage to 70 GB, while INT4/NVFP4 reduces it to 35 GB; the quantized choices still need cache, activations, compatible kernels and quality validation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can go wrong, and how do I fix it?
The model loads, then requests fail with out-of-memory errors
The initial load may not allocate the full KV cache or concurrent batch. Lower maximum context or concurrency, cap output tokens, enable paged or bounded cache if supported, and retest the worst-case request.
Quantization makes the model fit but quality or speed drops
Quantization can affect output quality, kernel support, throughput and context capacity. Compare validation quality and tokens per second at the target workload; use a higher-precision checkpoint or a larger GPU when the trade-off misses the service objective.
Multiple GPUs show poor scaling
Check whether communication is using the intended high-speed interconnect, whether tensor shards are balanced and whether the workload is large enough to amortize synchronization. For multi-node jobs, inspect network bandwidth, placement and collective-operation time.
Training runs out of memory only at larger batches
Peak activation and batch memory rise with micro-batch and sequence length. Reduce micro-batch, use gradient accumulation or activation checkpointing, and verify that optimizer and gradient storage are included in the measurement.
The preferred instance cannot be launched
Capacity, quota and regional inventory change independently of model fit. Check the provider’s current quota and capacity status, select an equivalent accelerator tier in another region or use a documented fallback size before changing the model configuration.
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Investigate CPU preprocessing, host-RAM pressure, storage throughput, network input and request scheduling. A memory-rich GPU cannot compensate for a data pipeline or host bottleneck, so profile end-to-end latency and throughput.
How should I account for cost and operations?
Compare total operating conditions rather than hourly GPU price alone. Include the number of GPUs, host CPU and RAM, local storage, network transfer, checkpoint storage, idle time, quota lead time, regional availability and whether spot interruptions are acceptable.
On-demand capacity is simpler for latency-sensitive serving. Spot or preemptible capacity can lower training cost when checkpoints and restart automation are reliable, but an interruption changes effective cost and completion time. For production inference, include the cost of keeping headroom for traffic bursts; a cheaper, fully packed card may require more replicas than a slightly larger card.
Before committing, verify the live provider catalog for the exact accelerator, instance shape, region, quota, billing model and available capacity. Specifications and prices can change independently of the GPU generation.
What is the practical decision rule?
First make the workload fit: weights plus KV cache, activations, training state, batch data and runtime overhead, with headroom. Then choose the smallest one- or multi-GPU configuration that meets context, concurrency, batch, latency and throughput targets. Only after those gates pass should memory bandwidth, interconnect, host resources, region, quota and price determine the final choice.
Frequently Asked Questions
Can a 70B model run on a single 24 GB GPU if I use quantization?
Not with the published weight estimates: a 70B INT4/NVFP4 version is about 35 GB before cache and runtime allocations. A 24 GB card therefore requires a different model size, offload strategy or a sharded deployment, and any offload approach must be tested for latency and throughput.
Is advertised GPU memory the same as usable memory?
No. Drivers, framework allocators, communication buffers and runtime kernels consume part of the capacity. Treat the catalog number as an upper bound and confirm the free memory after the exact software stack initializes.
Should I prioritize an H200 over multiple H100 GPUs?
Use the H200’s 141 GB capacity when a model or cache is difficult to shard, but compare the total configuration. Multiple H100s can provide more aggregate compute or throughput when the model parallelism and interconnect scale efficiently; the better choice depends on measured batch, context and communication behavior.
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There is no universal GPU-to-CPU ratio. Size them from tokenizer and preprocessing throughput, data-loader workers, checkpoint handling, network ingress and the number of concurrent requests, then verify that host utilization does not leave the accelerator idle.
When should I choose a cloud instance with eight GPUs?
Choose an eight-GPU shape when one GPU cannot hold the working set, when the required throughput exceeds smaller shapes, or when your parallelism strategy has been benchmarked at that scale. If the model fits on fewer GPUs and throughput is adequate, the smaller shape is usually easier to schedule and operate.
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