Alex Medick

Estimate LLM memory needs for real-world inference. Learn how context, KV cache, and GPU parallelism impact performance and scalability.

Estimating LLM Inference Memory Requirements

Successfully loading an LLM isn’t the same as running it at scale. In this guide, we break down the real memory demands of LLM inference - including model parameters, activations, and KV cache - and show how to estimate context length and concurrency limits with simple math. We also explore how tensor parallelism unlocks longer contexts and higher throughput by distributing memory across GPUs. Whether you’re debugging OOM errors or planning a production rollout, this article helps you avoid comm

Successfully loading an LLM isn’t the same as running it at scale. In this guide, we break down the real memory demands of LLM inference - including model parameters, activations, and KV cache - and show how to estimate context length and concurrency limits with simple math. We also explore how tensor parallelism unlocks longer contexts and higher throughput by distributing memory across GPUs. Whether you’re debugging OOM errors or planning a production rollout, this article helps you avoid common pitfalls and architect smarter deployments.

This article was written by Kyle Bell, VP of AI at TensorWave


Introduction

When deploying Large Language Models (LLMs) for inference workloads, engineers often encounter a critical disconnect between theoretical and practical memory requirements. Initial model loading may complete successfully, suggesting sufficient memory capacity. However, when processing real-world traffic, extended context windows, or concurrent request handling, memory-related failures frequently emerge.

This typically manifests as out-of-memory (OOM) errors when executing larger offline batch sizes, or recurrent timeouts when throwing too much traffic at an online endpoint. These failures often occur when resources are allocated to model weights (“does the model fit?”), but not enough thought has been given to capacity (“how much can it handle?”).

At TensorWave, we’ve observed a common pattern across organizations implementing LLM inference pipelines: successful initial deployment followed by unexpected memory constraints during scaling. The disparity between loading a model and maintaining its operational stability stems primarily from incomplete accounting of memory components that scale dynamically with inference parameters.

The effective memory capacity for LLM inference extends beyond the static model parameters to include dynamic components — particularly activation memory and key-value caches — which expand with context length and concurrency.

By examining each element of memory consumption and their interactions, we provide a simple equation to estimate context length capabilities in production environments and establish practical guidelines for deployment planning across multiple hardware configurations.


Components of LLM Memory

Graphics Processing Unit Video Random Access Memory (VRAM) represents a specialized form of high-bandwidth memory physically integrated with or positioned adjacent to the GPU die. Unlike system RAM, which communicates with the CPU through comparatively slower interfaces, VRAM operates on significantly wider buses that enable massive parallel data transmission essential for graphics and compute operations.

Modern AMD MI300X GPUs feature HBM3 (High Bandwidth Memory) delivering up to 5.3 TB/s of memory bandwidth — approximately 10–12 times faster than typical DDR5 system memory. This extreme bandwidth, combined with specialized memory hierarchies optimized for matrix operations, makes VRAM particularly suited for the parallel computations inherent in LLM inference.

However, this performance comes with physical limitations on capacity and sharing; VRAM must be managed as a constrained resource with careful consideration of its allocation across static and dynamic components that collectively determine the practical limits of inference workloads.


Model Parameters: The Static Foundation

Model parameters constitute the learned weights and biases that define the neural network’s behavior. These parameters represent the foundational component of memory consumption in LLM deployments and often the most significant portion of static memory allocation.

The memory requirement for storing model parameters can be calculated using the following formula:

Model Memory = Parameter Count * Precision

The precision term varies according to the quantization methodology employed:

 * FP16 (16-bit floating point): 2 bytes per parameter, representing the standard precision for most production deployments
 * FP8 (8-bit floating point): 1 byte per parameter, an emerging standard that requires hardware-specific support
 * INT4 (4-bit integer): 0.5 bytes per parameter, implementing aggressive quantization with associated quality-performance trade-offs

For a model with the scale of Llama-70B utilizing FP16 precision, the calculation yields:

70 GB * 2 = 140 GB

When deployed on hardware such as the AMD MI300X with 192GB capacity, this leaves approximately 52GB available for dynamic “runtime” memory.


Activation Memory: The Processing Buffer

During inference, intermediate computations create activations that must be temporarily stored. The transformer activations are quadratic with sequence length due to attention mechanisms, meaning each token’s attention across all other tokens creates an O(n²) relationship that makes long contexts prohibitively expensive.

Modern inference engines implement sophisticated optimizations like:

 * Flash attention for efficient attention computation
 * Activation recomputation for memory-compute tradeoffs
 * Optimized memory management that reuses buffers

These optimizations effectively linearize the relationship between sequence length and memory consumption during inference. The exact activation memory depends on implementation details across different inference engines, and as is often the case, best derived experimentally by profiling forward propagation.

However, we’ve found a buffer of 25% of model memory to be sufficient for most use cases:

Estimated Activation Memory ≈ Model Memory * 0.25

This factor represents a practical buffer that accounts for attention computations, layer outputs, and MLP activations after optimization. In practice, this approximation proves helpful for capacity planning, but should be carefully benchmarked when deploying into production.

For our 70B model (using Llama-70B on an AMD MI300X as our example):

140 GB * 0.25 = 35 GB


KV Cache: The Context Window Constraint

The Key-Value cache is where the bulk of dynamic memory is allocated. Unlike static parameters, KV cache grows linearly with both context length and concurrent requests, often becoming the largest memory consumer.

The per-token KV cache storage requirement:

KV Cache per Token = 2 * Precision * Layers * Hidden Dimension

For Llama-70B (80 layers, 8192 hidden dimension) at FP16:

2 * 2 Bytes * 80 * 8192 = 2.6 MB per Token

Total KV cache scales with usage:

Total KV Cache = KV Cache per Token * Context Length * Concurrent Requests

This is the equation that determines your practical context limits. For each concurrent request, every 1K tokens require ~2.6GB of additional memory. Even at a modest (for 2025) 16K context, KV cache for a single request can consume over 40GB; more than the activation buffer.


Calculating Maximum Context Length

For our 70B model on 192GB hardware (using Llama-70B on an AMD MI300X as our example):

Available for KV Cache = 192 GB – 140 GB – 35 GB = 17 GB

With 2.6 MB per token, we can calculate the maximum context length:

Max Length = Available for KV Cache / (KV Cache per Token * Concurrency)

For a single request:

Max Length = 17 GB / (2.6 MB * 1) ≈ 6,500 tokens

This means our model can handle a single request with a sequence length of ~6k, or roughly 6 concurrent requests of 1k each. This represents a significant constraint for applications requiring extended context windows, such as document analysis, multi-turn conversations, or code completion, all of which commonly require 8K-32K tokens of context.

This demonstrates why capacity planning must account for all memory components — not just the model parameters that receive the most attention in technical specifications. Without a comprehensive approach to memory allocation, you risk operational failure when attempting to handle realistic workloads.

However, this only takes into account single-GPU LLM inference. Next, we will explore how tensor parallelism across multiple GPUs can dramatically expand these constraints by distributing both static and dynamic memory components, enabling both longer context windows and higher throughput for production workloads.


Tensor Parallelism and Memory Distribution

When a single GPU’s memory constraints limit your context length or concurrency, tensor parallelism offers a solution by distributing the model across multiple GPUs. However, this isn’t just about having “more memory”, it fundamentally changes how memory components are allocated and scaled.


Understanding Tensor Parallelism

Tensor parallelism splits individual tensor operations across multiple GPUs. Unlike pipeline parallelism (which distributes model layers across GPUs), tensor parallelism divides single operations into smaller chunks processed in parallel.

For transformer models, this typically means partitioning the attention heads and MLP layers across GPUs. A 70B model with 80 attention heads might distribute 10 heads to each of 8 GPUs. This approach works particularly well for LLM inference because:

 1. The computations within transformer blocks are highly parallelizable
 2. The memory footprint scales near linearly with model size
 3. Communication overhead during inference is manageable

The key insight: tensor parallelism primarily addresses the static components of memory usage (model parameters and activations) while preserving or even expanding capacity for dynamic components (KV cache).

Tensor parallelism can also be used in conjunction with other parallelism techniques (such as pipeline parallelism), especially in multi-node inference. These will be discussed in a future article.


Memory Distribution Under Tensor Parallelism

With tensor parallelism across N GPUs, memory allocation changes as follows:


Model Parameters

Model weights are distributed evenly across GPUs. For our Llama-70B example across 8 GPUs:

140 GB / 8 = 17.5 GB per GPU


Activation Memory

Activation memory is maintained at 25% of model memory. For distributed weights this would come down to:

17.5 GB * 0.25 ≈ 4.4 GB per GPU

This scales linearly with weights after optimizations, and saves additional runtime memory on each GPU.


KV Cache

When using tensor parallelism, the KV cache is partitioned across GPUs in a manner consistent with how attention heads are distributed.

Since each GPU handles only its assigned portion of attention heads, it needs to store only the corresponding portion of the KV cache. This partitioning is a direct consequence of how tensor parallelism divides the computation and is not optional.


Recalculating Maximum Context Length

Let’s revisit our context length calculation with 8-way tensor parallelism on MI300X GPUs:

Available for KV Cache per GPU = 192 GB – 17 GB – 4.4 GB ≈ 170.6 GB

With 8 GPUs each having ~170 GB available for KV cache, and each GPU needing to store approximately 1/8th of the total KV cache with some overhead, our effective KV cache capacity becomes:

Effective KV Cache Capacity = 170 GB * 8 = 1,360 GB

Compared to our original 17 GB on a single GPU, this represents an 80x increase in KV cache capacity!

Applying our context length formula:

Max Length = Effective KV Cache Capacity / (KV Cache per Token * Concurrency)

For single-request inference:

Max Length = 1360 GB / 2.6 MB ≈ 523,000 tokens

Over half a million tokens! That’s a dramatic improvement over the ~6,500 tokens on a single GPU. As we can see, distributing weights across GPUs can free up runtime memory, and therefore context length.


The Concurrency-Context Trade-off

Here’s where tensor parallelism creates new possibilities. With 1,360 GB / ~523k tokens available for KV cache, you can choose different combinations of context length and concurrency:

Long Context Mode = 64k tokens * 8 requests = 512k total tokens

High Throughput Mode = 8k tokens * 64 requests = 512k total tokens

Balanced Model = 32k tokens * 16 requests = 512k total tokens

The 8-way tensor parallel setup doesn’t just solve our original context length problem, it transforms the memory constraint into a flexible resource you can allocate based on your specific workload requirements, as well as your memory / compute bottlenecks.

This explains why production deployments of large language models almost always use tensor parallelism, even when individual GPUs theoretically have enough memory for the model parameters. The value isn’t just in fitting the model, but in enabling workable combinations of context length and concurrency.


Conclusion

We’ve taken a deep dive into the mathematics of memory allocation for LLM inference, but these calculations are more than academic exercises, they directly impact your deployment strategy and user experience.

The journey from “my model loaded successfully” to “my model serves production traffic reliably” involves understanding these memory dynamics and making informed trade-offs. Here are the key takeaways to guide your LLM infrastructure planning:

 1. Context length is a function of available memory, not model size. Even massive models can support long contexts if you architect your deployment properly. Conversely, even small models can fail with modest context lengths if you don’t account for KV cache growth.
 2. Memory components scale differently across parallelization strategies. Tensor parallelism dramatically reduces the per-GPU cost of model parameters and activations while creating a shared pool of KV cache capacity that scales with GPU count.
 3. The choice between context length and concurrency is a deliberate trade-off. There’s no universal “best” configuration — the optimal setup depends on your specific workload patterns and user expectations. Modern inference engines like vLLM can help by dynamically allocating memory based on actual usage rather than worst-case scenarios.
 4. Measure twice, deploy once. These calculations provide a framework for capacity planning, but nothing replaces actual measurement on your target hardware with realistic workloads. Benchmark with representative query patterns and monitor memory usage across all components.

Whether you’re running inference workloads on your own infrastructure or leveraging cloud platforms like TensorWave, these principles apply universally. The difference between a deployment that works in demos and one that holds up under production load often comes down to this fundamental understanding of memory allocation.

In future articles, we’ll explore how these memory considerations interact with throughput, latency, and cost optimization, building on this foundation to create inference deployments that are not just technically feasible but economically viable and user-friendly.


References

Fu, Y. (2024) LLM Inference Sizing and Performance guidance, VMware Cloud Foundation (VCF) Blog. Available at: https://blogs.vmware.com/cloud-foundation/2024/09/25/llm-inference-sizing-and-performance-guidance/ (Accessed: 20 May 2025).

Shenoy, V. and Kiely, P. (2023) A guide to LLM inference and performance: Baseten blog, Baseten. Available at: https://www.baseten.co/blog/llm-transformer-inference-guide/#3500724-evaluating-gpus-for-llm-inference (Accessed: 20 May 2025).

Junda Chen, Y.Z. (2024) Throughput is not all you need: Maximizing goodput in LLM serving using prefill-decode disaggregation, Hao AI Lab @ UCSD (Alt + H). Available at: https://hao-ai-lab.github.io/blogs/distserve/ (Accessed: 20 May 2025).

UniversityPittsburghPAUSA, S.C. siyuanc3@andrew. cmu. edu C.M. et al. (no date) SLOs-Serve: Optimized Serving of Multi-SLO LLMs, Slos-serve: Optimized serving of Multi-SLO LLMS. Available at: https://arxiv.org/html/2504.08784v1 (Accessed: 20 May 2025).

Chen, C. (2022) Transformer Inference Arithmetic, kipply’s blog. Available at: https://kipp.ly/transformer-inference-arithmetic/ (Accessed: 20 May 2025).


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