Analytical models of peak VRAM consumption for LLM inference decompose memory into weight-storage, KV-cache, and activation terms parameterized by step count, tool invocations, and context expansion. We evaluate this decomposition empirically within a strictly scoped measurement study: a LangGraph-based CUDA-kernel-synthesis agent (AgentK), a 4-bit quantization family (Q4 K M), a single NVIDIA H10
Peak VRAM consumption in agentic code-synthesis workloads is decomposed into fixed weight storage, linear KV-cache growth, and dynamic activation overhead, with the KV-cache becoming the dominant bottleneck at long context lengths.
Memory Decomposition and Scaling The total VRAM requirement is calculated as: $$ \text{Total VRAM} = \text{Weights} + \text{KV Cache} + \text{Overhead} $$ Weights: Determined by model size and quantization; Q4_K_M is the recommended sweet spot, reducing VRAM by ~75% compared to FP16 with minimal quality loss. KV Cache: Grows linearly with context length (tokens). For a 9B model, expanding context from 8K to 32K tokens adds ~1–2 GB of VRAM, while 64K adds 2–4 GB. At 128K contexts, the KV cache can exceed the memory used by the weights themselves. * Overhead: Includes CUDA context, scratchpad activations, and framework allocations, typically consuming 0.5–1.5 GB.
Agentic Workload Dynamics In multi-agent or tool-use scenarios, VRAM stability is challenged by: Context Expansion: Code synthesis requires large context windows (32K–128K+), causing rapid KV-cache growth. Concurrent Agents: Without persistence, each agent’s KV cache consumes separate VRAM. Q4 KV-cache quantization allows fitting 4x more agent contexts than FP16 (e.g., 12 agents vs. 3 on 24 GB at 8K context). * Eviction and Reload: Persistent Q4 KV caches stored on disk allow sub-second reloads (~577 ms) compared to ~15.7 seconds for cold re-prefilling, hiding latency behind decode phases.
Optimization Strategies Quantization: Use Q4_K_M for weights and INT8/FP8 for KV caches to halve context memory penalties. Architecture: Models with Grouped Query Attention (GQA) minimize KV-cache footprint. * Persistence: Persisting quantized KV caches to disk enables handling of unbounded agent contexts within fixed VRAM limits, avoiding PCIe bandwidth cliffs associated with host RAM spilling.
This paper analyzes peak VRAM consumption in LLM-based agentic code synthesis, focusing on how memory demand evolves across multi-step agent execution rather than in a single inference call. It formalizes VRAM usage as a decomposition into weight storage, KV-cache, and activation memory, then parameterizes those terms by agentic workload variables such as step count, tool invocations, and context expansion. The empirical evaluation is deliberately narrow: a LangGraph-based CUDA-kernel-synthesis agent (AgentK) using a 4-bit Q4_K_M quantization family on a single high-end Hopper-class GPU. This scoped setup lets the authors isolate the memory behavior of an agentic LLM pipeline from the confounds of multi-GPU serving, multi-model routing, or heterogeneous hardware.
The central insight is that quantization reduces the weight term, but agentic workloads can make context-dependent memory the dominant and less predictable cost. As the agent accumulates prompts, tool outputs, generated code, and intermediate state, the KV-cache and activation footprint can grow in ways that single-turn inference models do not capture. The paper’s forecasting framework therefore provides a practical way to estimate how much VRAM an agent trajectory will require, how many steps or tool calls fit within a given memory budget, and where memory growth is most likely to become unstable. This is especially useful for deployment planning, where teams must decide whether a workload fits on one GPU, how aggressively context should be truncated or summarized, and whether a given quantization level is sufficient.
The work matters because agentic code synthesis is shifting GPU sizing from “model size plus batch size” to “model size plus trajectory shape.” As LLM agents become common for software generation, debugging, and kernel engineering, memory planning must account for repeated model calls, tool-mediated context expansion, and long-running state. By combining an analytical memory model with a controlled measurement study, the paper offers a reusable lens for reasoning about VRAM stability and forecasting in quantized agentic systems, rather than relying on ad hoc profiling or single-request heuristics.